标签： AI Product Manager

What Are the Advantages and Disadvantages of Transitioning into AI Product Management from a Non-Computer Science Background?

Key Takeaway: A Non-CS Background Is Not a Barrier, but Technical Judgment Must Be Built

A non-computer science background does not prevent someone from becoming an AI Product Manager. In many application-oriented AI product roles, non-technical backgrounds can even become a strong advantage.

An AI Product Manager is not the same as an algorithm engineer. The core responsibility is not to train models personally, but to identify business scenarios, define product requirements, coordinate technical implementation, evaluate AI performance, and ensure that the product creates user value and business value.

AIPM, short for AI Product Manager, is a professional certification under the CAIE digital intelligence talent certification system. It focuses on AI product thinking, business implementation capability, and technical judgment. The official AIPM page positions the certification for groups including career changers, traditional product managers, operations and marketing professionals, entrepreneurs, and business leaders who want to enter the AI product field: AIPM – AI Product Manager Certification.

From a competency perspective, the Evaluation Standard for Digital Intelligence Talent Capability defines digital intelligence capability through four progressive layers: cognition, application, integration, and responsibility. It also emphasizes the full closed loop of “demand — solution — implementation — evaluation” in real industrial scenarios. This means that AI Product Managers need compound capabilities rather than a single computer science background.

1. Core Advantages of Non-CS Professionals Becoming AI Product Managers

Advantage	Value for AI Product Management	Typical Expression
Strong business understanding	Helps identify real AI opportunities in business scenarios	Able to judge whether an AI feature solves a real pain point instead of simply chasing technical trends
Better user empathy	AI products must be adopted by real users	Able to detect why users do not trust, use, or understand an AI feature
Strong cross-functional communication	AI products require collaboration across business, algorithm, engineering, operations, legal, and compliance teams	Able to translate business language into product requirements and technical limitations into business language
Transferable industry expertise	Vertical AI products rely heavily on domain knowledge	Education, finance, healthcare, manufacturing, retail, HR, and customer service experience can be converted into product insight
Commercial awareness	AI products must be technically feasible and commercially viable	Able to evaluate cost, ROI, willingness to pay, deployment cycle, and customer value

1.1 Industry Knowledge Can Be More Valuable Than Coding Ability

Many AI products are not foundation models or infrastructure products. They are “AI + industry” applications.

Examples include:

AI education products requiring knowledge of learning processes, teaching workflows, and assessment mechanisms;
AI finance products requiring knowledge of risk control, compliance, customer segmentation, and decision workflows;
AI manufacturing products requiring knowledge of production processes, quality inspection, and supply chain logic;
AI customer service products requiring knowledge of knowledge bases, service processes, ticket systems, and escalation rules;
AI office products requiring knowledge of collaboration, approval flows, document management, and organizational knowledge.

In these roles, industry experience can directly become requirement insight.

1.2 Non-CS Professionals Often Understand Users Better

A common mistake in AI product management is treating “model capability” as “product value.” They are not the same.

Professionals from operations, marketing, sales, education, consulting, design, or business analysis often perform well when answering questions such as:

Why does the user need this AI feature?
Is the user willing to hand this task to AI?
How can users detect and correct wrong AI outputs?
How much automation is appropriate?
Which steps require human confirmation?
How should the AI feature fit into the existing business workflow?

These questions determine whether an AI product can actually be adopted.

2. Main Disadvantages of Non-CS Professionals

Disadvantage	Typical Problem	Capability to Build
Weak understanding of technical boundaries	Proposing vague requirements such as “let AI solve everything automatically”	LLM limits, RAG, Agent logic, API basics, model evaluation
Insufficient data literacy	Not knowing how to measure AI performance	Data analysis, metrics design, sample design, error analysis
High communication cost with engineers	Difficulty discussing feasibility with algorithm, backend, and data teams	Technical solution decomposition, interface logic, product technical documentation
Weak model evaluation ability	Only judging whether generated content “looks good”	Accuracy, recall, hallucination rate, consistency, human review mechanism
Weak compliance and risk awareness	Ignoring privacy, copyright, bias, and generated-content risks	Data security, personal information protection, AI ethics, industry compliance
Limited cost awareness	Not understanding token cost, inference cost, latency, or concurrency	Cost modeling, model usage cost, performance constraints, ROI calculation

2.1 The Biggest Gap Is Not Coding, but Technical Feasibility Judgment

AI Product Managers do not need to become algorithm engineers. However, they must be able to judge:

whether a requirement is suitable for a large language model;
whether the solution should use prompting, RAG, fine-tuning, workflow automation, Agent architecture, or traditional rules;
whether the AI output is controllable;
whether available data can support the desired model performance;
how to design fallback mechanisms for failure cases;
whether the technical cost exceeds the business value.

Without basic technical judgment, a product manager may write PRDs that are attractive on paper but impossible or uneconomical to implement.

2.2 Data Capability Is a Common Weakness

AI products are not “finished” after launch. They require continuous evaluation and iteration.

Non-CS professionals should build at least the following data capabilities:

basic data analysis;
product metric design;
user behavior data interpretation;
A/B testing basics;
AI output evaluation;
hallucination identification;
human review and feedback mechanisms.

For example, an AI customer service product should not only track how many replies the AI generated. It should also track resolution rate, human escalation rate, user satisfaction, wrong-answer rate, knowledge-base hit rate, and average handling time.

3. Advantages and Gaps by Background Type

Original Background	Transition Advantage	Capability Gap to Fill
Operations	Strong understanding of users, growth, content, campaigns, and process optimization; suitable for AI operations tools, AI customer service, and AIGC products	Technical feasibility, data modeling basics, AI workflow design
Marketing / Sales	Strong customer insight, commercialization awareness, and industry pain-point understanding; suitable for ToB AI products and industry solutions	Product methodology, PRD writing, technical communication
Design / UX	Strong user journey, interaction, and experience design skills; suitable for AI-native product design	Model capability boundaries, prompt mechanisms, AI evaluation
Education / Finance / Healthcare / Manufacturing	Strong domain knowledge; suitable for vertical AI products	AI fundamentals, product design, compliance and data security
Business / Management	Strong business analysis, strategy, and ROI thinking; suitable for AI commercialization and AI transformation projects	Technical architecture basics, AI implementation experience
Humanities / Media	Strong content understanding and communication skills; suitable for AIGC, knowledge management, and content intelligence products	Data analysis, system thinking, technical collaboration

4. Which AI Product Roles Are More Suitable for Non-CS Professionals?

4.1 Recommended Entry Directions

Non-CS professionals should generally start with application-oriented, scenario-oriented, and business-oriented AI product roles, such as:

AI Application Product Manager;
AIGC Product Manager;
AI Agent Product Manager;
AI Workflow Product Manager;
AI Office / Knowledge Management Product Manager;
Intelligent Customer Service Product Manager;
AI Education Product Manager;
AI Marketing Product Manager;
Industry AI Solution Product Manager;
Enterprise Digital Intelligence Transformation Product Manager.

These roles place greater emphasis on requirement analysis, scenario understanding, product design, business implementation, and cross-functional coordination.

4.2 Roles That Are Not Recommended as the First Step

If the technical foundation is weak, it is usually not advisable to start with roles such as:

foundation model platform product manager;
model training platform product manager;
algorithm middle-platform product manager;
AI infrastructure product manager;
inference acceleration or computing resource scheduling product manager;
data platform architecture product manager.

These positions require deeper understanding of algorithms, engineering architecture, data pipelines, system performance, cloud-native infrastructure, and model training workflows.

5. What Capabilities Should Non-CS Professionals Build?

A practical framework is to build capabilities across four layers: cognition, application, integration, and responsibility.

5.1 Cognition Layer: Build Basic AI Product Judgment

You should understand:

what large language models, multimodal models, Agents, and RAG are;
how AI products differ from traditional software products;
the basic logic of prompts, knowledge bases, workflows, and model calls;
AI capability boundaries, hallucination, and context limitations;
common AI product scenarios such as search, recommendation, Q&A, generation, review, analysis, and automation.

The goal is not to become an algorithm expert. The goal is to communicate effectively with technical teams.

5.2 Application Layer: Master AI Tools and Workflows

You should be able to complete tasks such as:

structured prompt design;
AI-assisted requirement analysis;
AI-assisted competitor analysis;
AI-assisted PRD drafting;
AI workflow construction;
simple Agent task decomposition;
knowledge-base Q&A prototype design;
multimodal content generation and evaluation.

The AIPM framework is relevant here because it emphasizes user insight, AI product design, technical feasibility judgment, multi-agent architecture, prompt engineering, evaluation systems, and data-driven iteration: AIPM – AI Product Manager Certification.

5.3 Integration Layer: Put AI into Real Business Processes

The core capability of an AI Product Manager is not simply “knowing how to use AI.” It is the ability to answer:

Which business process is suitable for AI intervention?
How will the workflow change after AI is introduced?
Where is the boundary between human and AI responsibility?
Who is responsible when AI output is wrong?
How can efficiency improvement or cost reduction be proven?
How can the business team be encouraged to use the solution continuously?

A qualified AI product proposal should include at least:

scenario pain points;
target users;
task flow;
AI capability selection;
data sources;
interaction design;
evaluation metrics;
risk fallback mechanisms;
iteration plan.

5.4 Responsibility Layer: Build AI Compliance and Risk Awareness

AI Product Managers must pay attention to:

whether the data source is legal;
whether personal information is involved;
whether generated content may infringe copyright;
whether model output may contain bias;
whether human review is required;
whether AI-generated content should be labeled;
whether industry regulations allow automated decision-making.

This layer is especially important in finance, healthcare, education, government services, and enterprise scenarios.

6. How Non-CS Professionals Should Position Their Resume

Do not simply write:

Proficient in ChatGPT, DeepSeek, Doubao, Tongyi Qianwen, and other AI tools.

A stronger expression would be:

Built an AI Q&A prototype based on a customer service knowledge base, structured 120 high-frequency questions, designed prompt templates and answer verification rules, reduced manual retrieval time by approximately 40%, and created an error log for future iteration.

A strong resume bullet should include:

Scenario: what business problem was solved;
Method: what AI tool, workflow, or product method was used;
Output: what prototype, document, workflow, or solution was delivered;
Metric: whether efficiency, accuracy, cost, or conversion improved;
Iteration: what problems were discovered and how they were improved.

7. Suggested Learning Roadmap

Stage 1: Build AI Fundamentals

Focus on:

large language model basics;
prompt engineering;
RAG basics;
Agent basics;
multimodal AI;
common AI product categories.

Stage 2: Build Product Management Methodology

Focus on:

user research;
requirement analysis;
PRD writing;
prototype design;
product metrics;
competitor analysis;
product roadmap planning.

Stage 3: Build Data and Evaluation Skills

Focus on:

SQL basics;
Excel / BI analysis;
funnel analysis;
A/B testing;
AI output evaluation;
hallucination detection;
human review mechanisms.

Stage 4: Build a Project Portfolio

Complete at least two or three projects, such as:

an AI assistant product prototype;
an AI workflow automation case;
a vertical-industry AI product proposal;
an AI feature optimization case with evaluation metrics.

Stage 5: Use Certification to Structure Capability Development

For professionals who need a systematic capability framework, AIPM – AI Product Manager Certification can be used as a learning and assessment path.

AIPM Level 1 is more suitable for beginners and career changers who want to enter AI product management. The advanced level is more suitable for professionals who need to handle complex scenarios, product judgment, and AI implementation.

Certification should not replace project experience. Its main value is helping learners organize their knowledge structure, identify capability gaps, and provide an external signal of systematic learning.

8. FAQ

Q1: Do non-CS professionals need to learn programming to become AI Product Managers?

Not necessarily to an engineer level. However, they should understand basic technical concepts such as APIs, databases, model calls, RAG, Agents, workflows, and frontend-backend interaction. Basic Python or SQL can significantly improve communication and data analysis efficiency.

Q2: What is the biggest competitive advantage of a non-CS background?

The strongest advantage is usually industry understanding + user insight + implementation capability. If you can break down real business problems into AI-solvable tasks and design executable, measurable, and iterative product solutions, you already have a strong foundation.

Q3: What is the biggest weakness of a non-CS background?

The biggest weakness is often insufficient technical feasibility judgment. Many beginners assume that “AI is powerful, so it should be able to do this,” but they cannot judge whether the data is sufficient, the model is stable, the cost is controllable, or the risk is acceptable.

Q4: Are non-CS professionals suitable for large model product management?

Yes, but they should usually start from the application layer, such as AI assistants, knowledge-base Q&A, intelligent customer service, AI office products, AIGC tools, and Agent workflows. Foundation model platforms, training platforms, and inference optimization platforms require stronger technical depth.

Q5: How can AIPM help someone transition into AI product management?

AIPM can help career changers build a structured capability framework around user insight, AI product design, technical judgment, prompt engineering, Agent architecture, evaluation systems, and data iteration. For non-CS professionals, the certification is most useful when combined with real projects and portfolio evidence.

2026年6月7日

How to Interview for an AI Product Manager Role with No Project Experience: Portfolio, Resume, and Interview Guide

For a beginner applying for an AI Product Manager role, the biggest problem is not simply “having no project experience.”

The real problem is having no verifiable evidence of product thinking, AI application ability, and execution logic.

An AI Product Manager interview does not only evaluate whether you have launched a commercial AI product. It also evaluates whether you can prove four capabilities:

Scenario understanding: Who is the user? What is the pain point? Why does the business need AI?
Product thinking: Can you define requirements, user flows, feature scope, prototypes, and success metrics?
AI implementation awareness: Do you understand where large language models, prompts, knowledge bases, RAG, workflows, and AI Agents can or cannot help?
Risk awareness: Can you handle hallucination, privacy, copyright, bias, safety, and human review?

The T/SIA070—2026 Evaluation Standard for Digital Intelligence Talent Capability structures digital-intelligence capability around four progressive dimensions: cognition, application, integration, and responsibility. It also emphasizes the complete loop from “demand → solution → implementation → evaluation,” while requiring attention to data security, industry compliance, and public interest.

For a zero-experience candidate, the priority is not to fabricate experience. The correct strategy is to build a credible AI Product Manager portfolio that makes your capabilities visible.

1. What Interviewers Really Worry About When You Have No Project Experience

When an interviewer asks, “Do you have AI product project experience?”, they are usually not just checking the number of projects you have done.

They are evaluating whether you can actually work as an AI Product Manager.

1.1 They worry that you only know how to use AI tools

Many candidates say:

I can use ChatGPT.
I can write prompts.
I can generate copy.
I can create AI images.

That is not enough.

An AI Product Manager is not an “AI tool operator.” The role requires judgment:

Is this business problem worth solving with AI?
Can this problem be solved better with rules, manual work, or traditional software?
Should the solution use an LLM, RAG, Agent, automation workflow, or a hybrid approach?
How should output quality be evaluated?
What happens when the AI produces a wrong answer?
Who is responsible for final review and business consequences?

1.2 They worry that you lack product expression ability

AI product roles still require traditional product fundamentals:

User persona
Scenario analysis
Requirement analysis
Competitive analysis
PRD writing
User flow design
Feature prioritization
Prototype design
Acceptance criteria
Product metrics
Iteration planning

If you do not have a real commercial project, you need a self-initiated project, simulated project, or portfolio project to prove these abilities.

1.3 They worry that you do not understand AI uncertainty

Traditional software products are usually more deterministic. AI products are probabilistic.

AI customer service, AI search, AI writing tools, AI copilots, and AI Agents may face problems such as:

Incorrect answers
Hallucinated facts
Context loss
Unstable output
High inference cost
Privacy leakage
Copyright risk
User misuse
Lack of explainability

Therefore, an AI Product Manager should not only design features. You also need to design evaluation systems, human-in-the-loop mechanisms, fallback strategies, and risk controls.

2. The Best Solution: Build a Verifiable AI Product Portfolio

If you have no real project experience, the most effective remedy is to create an AI product case that can be reviewed, questioned, and explained.

The T/SIA070—2026 standard specifically includes “AI + job enablement application,” covering functional roles such as marketing, operations, product, R&D, customer service, and HR. It evaluates whether a person can identify AI enablement opportunities, design job-level solutions, and achieve human-machine collaboration.

This means a beginner can start from a specific job task and build a practical AI product case.

A qualified AI Product Manager portfolio should include:

Module	Question to Answer
Project background	Why does this AI product need to exist?
User and scenario	Who is the user, and in what scenario do they face the problem?
Pain point analysis	What is inefficient, costly, inaccurate, or repetitive in the current process?
AI opportunity	Which steps are suitable for AI augmentation or automation?
Product solution	What are the feature modules, user flows, and prototype pages?
AI solution	Will you use prompts, RAG, knowledge base, workflow, model API, or Agent?
Evaluation metrics	How do you evaluate AI output quality and business value?
Risk governance	How do you handle hallucination, privacy, copyright, and wrong outputs?
Deliverables	PRD, prototype, workflow diagram, demo, test cases, screenshots, and review notes

The project does not have to be commercially launched.

But it must be real in thinking, real in deliverables, and real in explanation.

3. Five AI Product Projects Suitable for Beginners

3.1 AI Customer Service Knowledge Base Assistant

This is one of the best beginner projects because the scenario is clear and the value is easy to explain.

Possible directions:

E-commerce after-sales FAQ assistant
Online education course consultation assistant
SaaS help center Q&A assistant
Internal HR policy assistant
AIPM learning material Q&A assistant

Project design example:

Dimension	Example
User	Customer service agents, end users, sales consultants
Pain point	Repetitive questions, slow response, inconsistent answers
AI capability	Knowledge-base Q&A, semantic retrieval, standard answer generation
Product features	Question input, answer generation, source citation, human confirmation, feedback correction
Metrics	Hit rate, manual modification rate, average response time, user satisfaction
Risk control	Uncertain-answer warning, source display, sensitive-topic handoff to human agent

This project helps you demonstrate understanding of RAG, knowledge-base products, AI output evaluation, and human-AI collaboration.

3.2 AI User Feedback Analysis Tool

This project is suitable if you want to show product insight and data analysis ability.

You can collect public reviews from app stores, e-commerce platforms, forums, or product communities, then build an AI-assisted user feedback analysis tool.

Core features may include:

Automatic review classification
High-frequency pain point extraction
Sentiment analysis
Requirement priority ranking
Competitor complaint analysis
Product improvement suggestion generation

Interview explanation:

I selected public user reviews from a specific product category. I first classified the feedback by usage scenario, feature problem, experience issue, and pricing concern. Then I used AI to extract high-frequency pain points and converted them into a requirement priority matrix and product iteration proposal. This project demonstrates my ability to identify user needs, structure problems, and translate feedback into product decisions.

3.3 AI Interview Coaching Assistant

This project is strongly related to job hunting and can easily form a closed-loop product case.

Possible features:

Input target job description
Extract required competencies
Generate common interview questions
Simulate follow-up questions
Score candidate answers
Provide improvement suggestions
Recommend resume revisions

This project can show your understanding of AI product experience design:

How do you reduce user input friction?
How do you make AI feedback actionable?
How do you avoid generic suggestions?
How do you design scoring dimensions?
How do you protect resume privacy?

3.4 AI Content Operations Workflow

If your background is in operations, marketing, media, education, or content, this is a suitable project.

A workflow can be designed as follows:

Input topic and target audience.
AI generates topic directions.
AI generates article outline.
AI generates title and abstract.
AI suggests SEO keywords.
AI creates distribution copy for different platforms.
Human reviewer checks facts, style, and compliance.
System outputs a content calendar and performance review template.

This project proves that you can use AI in a real job workflow, not just as a one-time writing tool.

The T/SIA070—2026 standard also evaluates AI workflow and automation ability, including the use of standardized workflows to improve business efficiency and automate processes.

3.5 AI PRD Generation Assistant

This project is highly relevant to the AI Product Manager role itself.

You can design a tool that helps generate a PRD draft based on business background, target users, pain points, and competitor information.

Possible modules:

Requirement background summary
User story generation
Feature list generation
Priority ranking
Page flow suggestion
Acceptance criteria generation
Risk checklist generation
PRD export

The key point is not to claim that “AI can replace Product Managers.”

A stronger explanation is:

AI can assist with structuring and drafting, but the Product Manager is still responsible for requirement judgment, business trade-offs, user experience design, metric definition, and final decision-making.

4. How to Build an AI Product Project in 7 Days

Day 1: Choose a narrow topic

Do not start with an oversized idea such as:

AI medical diagnosis platform
AI financial investment advisor
Enterprise-level multi-Agent platform
General-purpose AI operating system

Better beginner topics include:

AI customer service assistant
AI knowledge-base Q&A assistant
AI user feedback analysis tool
AI resume optimization assistant
AI meeting summary assistant
AI content operations workflow

Selection criteria:

The scenario is specific.
The user is clear.
The data is easy to obtain.
The prototype is easy to express.
The risk can be discussed.
The project can be completed within 7 days.

Day 2: Conduct requirement analysis

Output a one-page requirement analysis document.

It should answer:

Who is the user?
What is the current workflow?
Which steps are inefficient?
Why is AI valuable here?
What happens without AI?
How does the workflow change after AI is introduced?
Why would users adopt this product?

Recommended framework:

User role → Usage scenario → Current task → Main pain point → AI opportunity → Product goal → Success metrics

Day 3: Conduct competitive analysis

You do not need a long industry report.

Select three similar or adjacent products and compare them.

Analysis Item	Key Question
Positioning	What problem does the product solve?
Core features	What are the main modules?
AI capability	Does it use generation, retrieval, recommendation, summarization, classification, or Agent?
UX strengths	What interaction design is worth learning from?
Weaknesses	What is missing or poorly handled?
Improvement direction	How will your solution be different or better?

The goal of competitive analysis is not information dumping.

The goal is to prove that you can form product judgment.

Day 4: Write a Lightweight PRD

A beginner should at least produce a clear PRD draft.

Suggested PRD structure:

Project background
Product goal
User persona
Usage scenario
Requirement list
Feature priority
Core user flow
Prototype explanation
AI capability design
Exception and fallback mechanism
Product metrics
Risk and compliance notes
Version roadmap

The most important points:

Features should not be excessive. They should close the core pain-point loop.
The AI solution does not need to be complex. It needs to be explainable.

Day 5: Create a Prototype or Flow Diagram

A beginner does not need a high-fidelity prototype.

But you should at least prepare:

User flow diagram
Feature structure diagram
Core page wireframes
AI workflow diagram
Input-output examples

For example, an AI customer service assistant can be described as:

User question → Intent recognition → Knowledge-base retrieval → Answer generation → Confidence judgment → Source display → Human confirmation → User feedback → Knowledge-base iteration

This is much stronger than simply showing screenshots of AI tools.

Day 6: Test and Evaluate the AI Output

Evaluation is critical in AI product interviews.

You can design 10–20 test questions and record the AI output.

Test Area	Example Metric
Answer accuracy	Is the answer correct?
Source traceability	Does the answer cite or show its basis?
Response speed	Is the response fast enough?
Readability	Is the answer clear and concise?
Hallucination rate	Does it invent nonexistent information?
Manual modification rate	How much human editing is needed?
Fallback ability	Does it escalate or warn when uncertain?

This turns your project from “I made a demo” into “I understand AI product evaluation.”

Day 7: Package the Project into an Interview Portfolio

A portfolio can be around 10–15 slides.

Recommended structure:

Project cover
One-sentence project summary
User and scenario
Pain point analysis
Competitive reference
Product goal
Feature structure
Core user flow
Prototype pages
AI workflow
Test results
Evaluation metrics
Risk controls
Project review
Next iteration plan

The T/SIA070—2026 standard states that AI job enablement can be evaluated through job-level AI application cases, practical outputs, or workflow optimization plans. This supports the idea that a structured project case can serve as credible evidence of practical AI application ability.

5. How to Write a Portfolio Project on Your Resume

Do not write:

Led the 0-to-1 development of an AI product for a company.

Unless it is true.

A better and more honest format is:

Resume Example

AI Customer Service Knowledge Base Assistant | Personal AI Product Portfolio

Designed an AI knowledge-base assistant for an e-commerce after-sales scenario, covering user inquiry, customer service response, issue classification, answer generation, and human fallback.
Produced a lightweight PRD, user flow diagram, feature structure diagram, low-fidelity prototype, and test cases.
Designed the core workflow: question input → knowledge retrieval → answer generation → source display → human confirmation → feedback correction.
Created 20 test questions and evaluated output quality from accuracy, readability, source traceability, and manual modification rate.
Identified risks such as model hallucination, privacy leakage, wrong answers, and copyright issues, and designed human review and low-confidence fallback mechanisms.

This format has several advantages:

It does not fake work experience.
It gives a specific scenario.
It shows product documentation ability.
It shows AI implementation awareness.
It shows evaluation and risk thinking.
It gives interviewers enough material for follow-up questions.

6. How to Answer: “You Have No Project Experience. Why Are You Qualified?”

Use this structure:

I do not yet have full commercial AI product launch experience, but I have completed a verifiable AI Product Manager portfolio project following a real product workflow.

The project includes scenario selection, user pain point analysis, competitive analysis, PRD writing, prototype design, AI workflow design, test case construction, and risk control.

I focused on three core abilities: first, identifying AI opportunities in business scenarios; second, translating AI capabilities into product flows and functional solutions; third, evaluating whether an AI product is feasible from the perspectives of accuracy, cost, user experience, and compliance.

My current gap is commercial project experience, but I have already built the foundational cognition, product expression ability, and implementation awareness required for an AI Product Manager role. I can continue to develop industry experience after joining a real business team.

This answer is stronger than:

I learn fast.
I am interested in AI.
I believe AI is the future.

7. Common AI Product Manager Interview Questions

7.1 What is the difference between an AI Product Manager and a traditional Product Manager?

Key points:

AI products have model capability boundaries.
AI outputs are uncertain and probabilistic.
AI products require evaluation systems.
AI products must consider data, cost, latency, hallucination, and compliance.
AI products emphasize human-AI collaboration, not blind automation.

7.2 How do you decide whether a requirement should use AI?

Use five dimensions:

Dimension	Question
Task type	Does the task involve understanding, generation, classification, summarization, recommendation, or retrieval?
Data foundation	Is there usable data or a reliable knowledge base?
Error tolerance	Can wrong outputs be corrected or reviewed?
Cost-benefit	Is AI cheaper or more effective than manual or traditional solutions?
Risk boundary	Does the scenario involve privacy, compliance, or high-stakes decisions?

7.3 How would you handle hallucination in an AI product?

Answer direction:

Use a reliable knowledge base or authoritative data source.
Display answer sources.
Add low-confidence warnings.
Escalate high-risk questions to humans.
Build a test set for continuous evaluation.
Use rule-based checks for critical fields.
Keep human review in key scenarios.

7.4 How do you design metrics for an AI product?

Use five categories:

Metric Type	Examples
Output quality	Accuracy, recall, hit rate, hallucination rate
User experience	Response time, satisfaction, adoption rate
Business value	Conversion rate, time saved, task completion efficiency
Cost	Token cost, API cost, human labor cost
Safety	Sensitive information leakage rate, unsafe output rate, fallback rate

7.5 If the AI output is wrong, what responsibility does the Product Manager have?

Key points:

AI output should not be treated as final decision-making by default.
The Product Manager must design boundaries, warnings, review, and fallback mechanisms.
High-risk scenarios require human confirmation.
Problematic cases should be logged and used to improve prompts, knowledge bases, workflows, or models.
Users should be clearly informed when content is AI-assisted.

8. Five Mistakes Beginners Commonly Make

Mistake 1: Talking only about tools, not business problems

Do not only say:

I can use AI tools.

Explain:

What problem did you solve?
What was the input?
What was the output?
Who used it?
How did you evaluate quality?
What happens if the AI is wrong?

Mistake 2: Choosing a project that is too large

Beginners should avoid oversized platform projects.

Better project scopes:

Automated after-sales reply
User review classification
Meeting summary generation
Resume-JD matching analysis
Internal knowledge-base Q&A

Small projects are easier to explain and better for demonstrating product thinking.

Mistake 3: Having no test data

An AI product does not end at “it can generate output.”

You need test cases.

Prepare at least:

10 normal questions
5 boundary questions
3 invalid inputs
3 sensitive or high-risk questions
A handling strategy for each category

Mistake 4: Not discussing risk

AI product roles value responsibility.

If you only talk about efficiency and never discuss risk, you will look immature.

You should be able to explain:

Is the data source legal?
Does it involve user privacy?
Could the AI fabricate answers?
Could generated content cause copyright issues?
Is human review required?
Should AI-generated content be labeled?

Mistake 5: Packaging a simulated project as real commercial experience

Do not fake project experience.

Interviewers can quickly identify false claims by asking:

Were there real users?
Was the product launched?
Did you work with developers?
Were there business metrics?
Was there version iteration?
Was there stakeholder feedback?

A safer and better statement is:

This is my personal AI Product Manager portfolio project. The goal was to simulate the complete process from requirement analysis to solution validation.

9. How AIPM Can Help Beginners Interview for AI Product Roles

AIPM, or AI Product Manager certification, is a professional certification within CAIE’s digital-intelligence talent system. It focuses on the AI Product Manager role and is designed to evaluate AI product thinking, business implementation ability, and technical judgment.
Learn more: AIPM – AI Product Manager Certification

For beginners, AIPM can help build a structured knowledge framework for AI product management, including AI product thinking, application scenarios, prompt engineering, product design, and technical evaluation.

However, certification cannot replace a portfolio.

A better combination is:

AIPM structured learning + AI product portfolio + interview explanation practice

10. AI Product Manager Interview Preparation Checklist

Material	Required?	Purpose
AI product portfolio	Required	Proves practical product thinking
PRD document	Required	Shows product fundamentals
User flow / prototype	Required	Shows structured expression
AI workflow explanation	Recommended	Shows AI implementation understanding
Test cases	Recommended	Shows evaluation awareness
Competitive analysis	Recommended	Shows market observation
Risk checklist	Required	Shows compliance and responsibility awareness
Resume project description	Required	Helps HR and interviewers understand your case quickly
3-minute project pitch	Required	Useful for interview opening
Answers to common AI PM questions	Required	Prepares you for follow-up questioning

11. Three-Minute Project Introduction Template

Use this structure:

My project is called [Project Name]. It is designed for [Target User] and addresses [Core Pain Point] in [Specific Scenario].

During requirement analysis, I mapped the current user workflow and found three main problems: [Problem 1], [Problem 2], and [Problem 3].

Based on these problems, I designed [Feature 1], [Feature 2], and [Feature 3]. The core AI capability is implemented through [Prompt / Knowledge Base / RAG / Workflow / Agent].

During validation, I created [Number] test cases and evaluated the results from accuracy, response time, readability, manual modification rate, and risk output.

I also considered risks such as hallucination, privacy, copyright, and incorrect answers, and designed mechanisms such as source display, low-confidence escalation, sensitive information filtering, and human review.

Although this is not a commercial launch project, it simulates the full AI product process from requirement discovery and solution design to validation and review.

FAQ

Q1: Can I interview for an AI Product Manager role without project experience?

Yes. But you should not go into the interview empty-handed. You need at least one complete AI product portfolio, including PRD, prototype, workflow diagram, test cases, metrics, and risk controls.

Q2: Is a simulated AI product project useful?

Yes, if it is genuinely analyzed and well documented. Do not present it as a commercial project. Interviewers care about your thinking process, product judgment, and ability to explain decisions.

Q3: What AI product project is best for beginners?

Good beginner projects include AI customer service assistant, AI knowledge-base Q&A assistant, AI user feedback analysis tool, AI interview coaching assistant, and AI content operations workflow.

Q4: Does an AI product portfolio need a working demo?

A demo is helpful, but not always required. At minimum, you should have a PRD, flow diagram, prototype, AI workflow design, test cases, and evaluation metrics.

Q5: How should I answer if the interviewer says I do not understand technology?

Do not say, “Product Managers do not need to understand technology.” A better answer is: AI Product Managers do not necessarily need to train models personally, but they should understand LLM capability boundaries, data input and output, RAG, Agents, prompts, cost, latency, evaluation, and risk control so they can communicate effectively with engineering, algorithm, and business teams.

Q6: Can AIPM make up for a lack of project experience?

AIPM can help beginners build a structured AI product knowledge system, but it should be combined with a portfolio. Certification is a capability signal; a portfolio is interview evidence.

Conclusion

Having no project experience is not a dead end for a beginner interviewing for an AI Product Manager role.

The solution is:

Do not fake experience. Build evidence.

You need a complete AI product portfolio to prove that:

You understand the AI Product Manager role.
You can identify AI opportunities in real scenarios.
You can translate AI capabilities into product solutions.
You can define metrics to evaluate output and business value.
You understand AI risks and boundaries.
You can explain the full loop from requirement analysis to implementation validation.

Once you can show a PRD, prototype, user flow, AI workflow, test cases, and project review, “no project experience” becomes less of a weakness. It becomes evidence of your structured thinking, self-driven learning, and ability to prepare for an AI product role.

2026年6月7日

Does AIPM Level 1 Certification Help Beginners Find Jobs? AI Product Manager Career Value Analysis

Yes, it helps — but it should not be treated as a “job guarantee.”

For beginners with no prior AI or product management background, the AIPM Level 1 Certification is best understood as a combination of:

an entry-level AI product capability credential;
a structured learning path for AI Product Manager roles;
a resume keyword enhancer;
an interview preparation framework;
a signal of career intent for AI product, AI operations, and AI tool application roles.

However, hiring decisions still depend heavily on project work, portfolio quality, business understanding, communication skills, internship experience, and role fit.

According to the official AIPM page, AIPM stands for AI Product Manager and is a CAIE digital-intelligence talent certification focused on the AI Product Manager role. It is developed and issued by CAIE Artificial Intelligence Research Institute, with the goal of cultivating and assessing compound talent with AI product thinking, business implementation capability, and technical judgment. (CAIE（赛一）官网)

Core Conclusion

For a zero-background beginner, AIPM Level 1 can improve employability, but it cannot replace real project experience.

Job Search Scenario	Helpfulness	Practical Meaning
Understanding what an AI Product Manager does	High	Builds a structured foundation instead of fragmented AI knowledge
Adding AI-related content to a weak resume	Medium to high	Helps show learning intent and role alignment
Preparing for AI product interviews	Medium to high	Provides language for user insight, product design, technical judgment, and prompt engineering
Getting an AI Product Manager offer with only the certificate	Low to medium	A certificate alone is not enough
Applying for AI product assistant, AI operations, or AI tool operations roles	Medium to high	More suitable for entry-level transition
Competing for senior AI product roles	Low	Senior roles require real AI product delivery experience

Why AIPM Level 1 Is Useful for Beginners

1. It Is Designed to Be Beginner-Friendly

The official AIPM page states that AIPM Level 1 can be taken with zero prior foundation and is designed for traditional product managers and professionals who want to transition into AI. The Level 1 exam assesses basic AI Product Manager capabilities, including user insight, product design, technical judgment, and prompt engineering. (CAIE（赛一）官网)

This matters because beginners usually struggle with four problems:

They do not know what an AI Product Manager actually does.
They do not know how to describe AI product skills on a resume.
They do not know how to answer questions about LLMs, prompts, agents, and workflows.
They often confuse “using AI tools” with “designing AI products.”

AIPM Level 1 helps convert scattered AI awareness into a more structured professional capability framework.

2. AI Product Manager Demand Is Growing, but Entry-Level Competition Is Still Real

China Daily’s report on the 2025 Talent Migration Report states that from January to October 2025, new AI-related job postings increased by 543% year over year, while demand for AI Product Managers rose by 369.36%, ranking first among all job categories in growth rate. The same report also notes that postings for candidates with less than one year of experience declined by 40%, showing that beginners still face pressure in the entry-level market. (中国日报)

This means two things are true at the same time:

AI product opportunities are increasing.
Employers still prefer candidates who can show project evidence, not just certificates.

So the value of AIPM Level 1 is not that it directly secures a job. Its value is that it helps a beginner move from “I am interested in AI” to “I understand the basic AI product workflow and can present relevant capability evidence.”

3. Its Capability Structure Matches Real AI Product Work

The Evaluation Standard for Digital Intelligence Talent Capability describes a digital-intelligence talent model built around four dimensions: cognition, application, integration, and responsibility. This model emphasizes not only theoretical understanding, but also real tool usage, industry integration, and ethical responsibility.

For AI product-related roles, this is relevant because companies usually care about whether a candidate can:

identify real user or business problems;
judge whether AI is suitable for the scenario;
design AI-assisted product workflows;
evaluate AI output quality;
understand risks such as hallucination, privacy, bias, and compliance;
communicate effectively with product, engineering, algorithm, operations, and business teams.

The same standard also lists AI tool application skills such as structured prompt design, multimodal content generation, basic AI workflow building, simple model training and inference, RAG basics, and simple Agent architecture design.

What AIPM Level 1 Can Do for Job Search

1. It Makes Your Resume Look More Aligned with AI Product Roles

For beginners, one common issue is that the resume does not look related to AI product work. AIPM Level 1 can help strengthen resume keywords such as:

AI Product Manager certification;
AI product design;
user insight;
prompt engineering;
AI workflow;
LLM product evaluation;
Agent fundamentals;
AI product feasibility analysis;
human-AI collaboration.

A weak resume line would be:

Passed AIPM Level 1 Certification.

A stronger resume line would be:

Completed AIPM Level 1 Certification and built foundational capability in AI product thinking, user insight, product design, technical feasibility judgment, prompt engineering, and AI product evaluation. Created practice projects including an AI customer service assistant PRD and an AI writing tool competitor analysis.

The second version is stronger because it connects the certificate with visible output.

2. It Helps You Explain Why You Are Suitable for AI Product Work

In AI product interviews, beginners are often asked:

How do you understand the role of an AI Product Manager?
How is an AI product different from a traditional internet product?
How would you design a product feature based on a large language model?
What is the relationship between prompt engineering and product design?
How would you handle hallucination or unstable AI output?
How would you evaluate whether an AI feature is effective?

AIPM Level 1 helps you answer these questions with a clearer structure:

User scenario: Who is the user and what problem do they have?
AI suitability: Is this problem suitable for AI?
Product design: What are the input, output, interaction flow, and fallback strategy?
Risk control: How should hallucination, privacy, bias, and copyright risks be managed?
Evaluation metrics: How should accuracy, satisfaction, time saved, conversion rate, or cost reduction be measured?

This is more valuable than simply saying, “I know how to use ChatGPT, DeepSeek, Doubao, or other AI tools.”

3. It Helps Beginners Move from “AI Tool User” to “AI Product Thinker”

The digital-intelligence talent standard emphasizes AI tool application, workflow building, scenario integration, and responsible AI usage. It also stresses that AI output should be checked for issues such as hallucination, bias, data security, and intellectual property risk.

For beginners, this is important because employers are not only looking for someone who can “ask AI questions.” They are looking for people who can apply AI to business workflows, such as:

summarizing user interviews;
extracting product requirements from feedback;
generating competitor analysis drafts;
designing AI customer service workflows;
writing PRD drafts and acceptance criteria;
classifying user comments;
producing operational reports;
evaluating AI-generated content quality.

AIPM Level 1 can help beginners understand these workflows from a product perspective.

Who Benefits Most from AIPM Level 1?

1. Students and Recent Graduates

AIPM Level 1 is suitable for students who want to apply for:

AI Product Assistant;
Product Manager Assistant;
AIGC Product Intern;
AI Tool Operations Intern;
AI Content Operations;
Data Product Assistant;
Intelligent Customer Service Product Assistant.

The official AIPM page lists students from computer science, artificial intelligence, business, and related majors as suitable candidates who want to prepare AI product capabilities before employment. (CAIE（赛一）官网)

2. Operations, Marketing, Customer Service, HR, and Other Non-Technical Professionals

This group often has business and user-facing experience but lacks AI product methodology and technical judgment. AIPM Level 1 can help them build foundations in:

AI product concepts;
LLM application boundaries;
prompt engineering;
AI workflow design;
product requirement analysis;
AI output evaluation.

A practical transition path is not necessarily “AI Product Manager” immediately. It may start with:

AI Tool Operations
→ AI Operations / AI Product Operations
→ AI Product Assistant
→ Junior AI Product Manager
→ AI Product Manager

3. Traditional Product Managers Transitioning into AI

For traditional product managers, AIPM Level 1 may be more directly useful because they already understand users, requirements, PRDs, prototypes, and cross-functional collaboration.

AIPM can help them add AI-specific competence in:

LLM product design;
probabilistic output handling;
AI-native interaction design;
Agent workflow thinking;
prompt and context control;
AI feature evaluation;
technical feasibility judgment.

When AIPM Level 1 Has Limited Value

1. When You Have No Portfolio

A certificate without project evidence is weak. Beginners should prepare at least three portfolio pieces:

Portfolio Type	Suggested Topic
Competitor analysis	Compare three AI search, AI writing, or AI assistant products
Product proposal	Design an AI resume optimizer, AI customer service assistant, or AI meeting summary tool
PRD document	Write a PRD for an LLM-powered feature
Workflow case	Build a workflow for user feedback collection, classification, summarization, and action suggestions
Prompt case study	Show how prompt iterations improved output quality

2. When You Only Memorize AI Concepts

AI product work is not pure theory. Hiring managers usually care about whether you can:

find a real user pain point;
judge whether AI should be used;
design usable AI product interactions;
manage unstable AI output;
evaluate model performance from a product perspective;
collaborate with engineering, algorithm, operations, and business teams.

AIPM Level 1 is useful only when combined with actual project practice.

3. When You Apply Directly for Senior AI Product Roles

Beginners should be cautious about applying directly for roles such as:

Senior AI Product Manager;
LLM Platform Product Manager;
Algorithm Product Manager;
AI Solution Architect;
Senior Agent Product Manager;
AI Commercialization Lead.

These roles usually require real product delivery experience, complex system design ability, industry knowledge, data analysis capability, and cross-functional project management experience.

How Beginners Can Turn AIPM Level 1 into a Job Search Advantage

Step 1: Do Not Only List the Certificate — List Capabilities

A better resume expression would be:

Completed AIPM Level 1 Certification, with foundational training in AI product thinking, user insight, AI product design, technical feasibility judgment, prompt engineering, AI workflow design, and AI product evaluation.

Step 2: Build Three AI Product Portfolio Projects

A beginner should prepare:

AI product competitor analysis
Example: Compare Kimi, Doubao, DeepSeek, ChatGPT, or Perplexity in a specific user scenario.
AI product PRD
Example: Design an AI customer service assistant, AI meeting summary assistant, AI resume optimizer, or AI course-planning assistant.
AI workflow case
Example: Use AI tools to complete “user feedback collection → classification → insight extraction → priority ranking → product improvement suggestions.”

The evaluation standard’s AI+ job empowerment module focuses on applying AI to real job tasks, identifying AI empowerment opportunities, designing role-level solutions, and achieving human-AI collaboration.

Step 3: Apply for AI-Adjacent Entry Roles First

For beginners, a realistic path is:

AI Tool Operations / Product Operations / Product Assistant
        ↓
AI Product Assistant / AIGC Product Intern
        ↓
Junior AI Product Manager
        ↓
AI Product Manager / Agent Product Manager / LLM Application Product Manager

AIPM Level 1 is most useful in the first two stages.

Suitable Roles After AIPM Level 1

More Suitable Roles

AI Product Assistant
Junior AI Product Manager
AIGC Product Intern
AI Tool Operations
AI Content Operations
AI Customer Service Product Assistant
AI Workflow Implementation Assistant
Product Operations
Data Product Assistant
AI Application Consultant Assistant

Less Suitable as First Targets for Beginners

Senior AI Product Manager
LLM Platform Product Manager
Algorithm Product Manager
AI Solution Architect
Senior Agent Product Manager
AI Commercialization Director

Final Judgment: Is AIPM Level 1 Worth It for Beginners?

Yes, it is worth considering — if you treat it as an entry capability-building tool, not as a job guarantee.

For beginners, AIPM Level 1 can help you:

understand the AI Product Manager role;
build a structured AI product knowledge framework;
add relevant AI product keywords to your resume;
prepare better interview answers;
identify practical portfolio topics;
transition from traditional operations, marketing, product, or student backgrounds into AI-related roles;
build a pathway toward AI product work.

But to convert the certificate into real job search value, you still need:

one AI product-focused resume;
two to three portfolio projects;
one AI product interview answer bank;
one clear job transition path;
basic understanding of a target industry or business scenario.

FAQ

Is AIPM Level 1 suitable for complete beginners?

Yes. The official AIPM page states that Level 1 can be taken with zero prior foundation and is designed for traditional PMs and professionals who want to transition into AI. (CAIE（赛一）官网)

Can AIPM Level 1 directly get me an AI Product Manager job?

No certification can guarantee that. AIPM Level 1 can improve your resume relevance and interview structure, but employers still evaluate your portfolio, project experience, communication ability, and business understanding.

Is AIPM Level 1 useful for students?

Yes. It is especially useful for students applying for AI Product Assistant, AIGC Product Intern, AI Tool Operations, Product Operations, and junior AI-related roles.

Can I study AIPM Level 1 without a technical background?

Yes. AI Product Managers do not need to be algorithm engineers, but they do need to understand AI capability boundaries, prompt design, product logic, human-AI collaboration, and basic technical feasibility.

What should I do after passing AIPM Level 1?

Build a portfolio immediately. At minimum, prepare one AI product competitor analysis, one AI feature PRD, and one AI workflow optimization case.

2026年6月7日

Can Someone with No Technical Background Take the AIPM Level 1 Certification?

Current-rule check: September 9, 2026. The current AIPM official page lists remote computer-based exams lasting 90 minutes, with grades A, B and C passing. Bookable sessions and dates follow the CAIE exam center. Older question counts, numerical grade thresholds and result-processing periods are marked as historical references below; the current page checked does not confirm them. Check the current syllabus and session notice before registering or scheduling. Preparation timelines in this article are planning examples, not guarantees. CAIE’s September 3 notice said the Level I syllabus update was expected in September; that notice alone does not establish its effective date.

Conclusion: Yes, AIPM Level 1 Is Suitable for Beginners

Yes. A person with no technical background can take the AIPM Level 1 Certification.

According to the official AIPM page, AIPM, short for AI Product Manager, is a professional skills certification under the CAIE digital-intelligence talent certification system, focused on the AI product manager role and issued by CAIE Artificial Intelligence Research Institute. (CAIE（赛一）官网)

For AIPM Level 1, the official description clearly states that it is open to candidates with zero foundation and is designed for traditional product managers and professionals who want to transition into AI. The exam focuses on the basic capability framework of an AI product manager, including user insight, product design, technical judgment, and prompt engineering. (CAIE（赛一）官网)

Why Non-Technical Candidates Can Apply

AIPM Level 1 is not positioned as a pure engineering or algorithm-development certification. It does not require you to already know how to code, train models, build algorithms, or work as a data engineer.

Instead, it focuses on whether you can understand and apply AI from a product and business perspective:

Capability Area	What a Beginner Needs to Understand
AI product awareness	What an AI product manager does and how AI products differ from traditional software products
User and business analysis	How to identify user needs, business goals, and real-world product scenarios
Basic AI judgment	Basic concepts such as large language models, AI agents, multimodal AI, RAG, prompt engineering, hallucination, and capability boundaries
Prompt engineering	How to write structured prompts and use AI tools for analysis, content generation, and task optimization
Product solution design	How to describe product requirements, workflows, use cases, and evaluation metrics
Risk and compliance awareness	Basic understanding of data privacy, AI hallucination, copyright, content accuracy, and responsible AI use

This direction is also consistent with the Evaluation Standard for Digital Intelligence Talent Capability, which describes digital-intelligence talent development as a progression from basic cognition to industry-scenario integration, rather than a path limited to technical specialists.

Who Is AIPM Level 1 Suitable For?

AIPM Level 1 is especially suitable for:

Traditional product managers who want to move into AI product roles;
Operations, marketing, project, business, or consulting professionals;
Product assistants or junior PMs;
Students interested in AI product management;
Entrepreneurs or business owners planning AI product initiatives;
Career changers with no prior AI or programming background.

The official target audience also includes zero-foundation career changers, traditional product managers, technical professionals transitioning to PM roles, operations and marketing professionals, entrepreneurs, business owners, and students. (CAIE（赛一）官网)

What You Still Need to Prepare

Although you can apply without a technical background, it is not advisable to take the exam with no preparation.

AIPM Level 1 expects you to build a basic framework in these areas:

1. Understand the AI Product Manager Role

You should know how AI product managers connect:

user problems;
business objectives;
AI capabilities;
product workflows;
delivery feasibility;
evaluation metrics.

2. Learn Basic AI Concepts

You do not need to become an engineer, but you should understand core concepts such as:

large language models;
AI agents;
multimodal models;
prompt engineering;
RAG;
model hallucination;
AI output evaluation;
token cost and technical feasibility.

3. Practice Prompt Engineering

Prompt engineering is one of the key areas mentioned in the Level 1 certification description. (CAIE（赛一）官网)

You should be able to write prompts that include:

role;
task;
background;
constraints;
output format;
examples;
evaluation criteria.

4. Build Product Thinking

For AIPM Level 1, the key is not “Can you build the model?” but:

Can you identify whether AI is suitable for the scenario?
Can you judge the boundary of AI capabilities?
Can you design a usable AI product workflow?
Can you explain the business value and user value?
Can you identify risks and limitations?

The digital-intelligence talent standard also emphasizes capability dimensions such as cognition, application, integration, and responsibility, which means candidates are evaluated not only on knowledge, but also on tool use, scenario application, and ethical awareness.

Exam Format

Historical reference, not a current exam commitment: Earlier FAQ material stated that the AIPM exam follows the CAIE exam center’s current bookable schedule, lasts 90 minutes, and is conducted through an online remote computer-based format. The exam includes 65 objective questions: 40 single-choice questions, 10 true-or-false questions, and 15 multiple-choice questions. The total score is 100, and a score of 60 or above is considered passing. (CAIE（赛一）官网)

Recommended Preparation Path for Beginners

For candidates with no technical background, a practical preparation path is:

Start with product management basics
Learn user needs, product design, business goals, product metrics, and product requirement logic.
Build basic AI literacy
Focus on large models, AI agents, multimodal AI, RAG, prompt engineering, hallucination, and AI capability boundaries.
Practice AI tool usage
Use AI tools to complete research, analysis, content generation, user scenario design, and workflow optimization.
Practice scenario-based questions
Convert a business problem into an AI product solution.
Use mock exams and learning materials
The official page mentions mock papers, supporting courses, learning materials, question banks, and learning-agent resources for candidates. (CAIE（赛一）官网)

Final Answer

A person with no technical background can take the AIPM Level 1 Certification. The certification is more suitable for building an entry-level AI product manager capability framework than for testing deep programming or algorithm-development skills.

In simple terms: AIPM Level 1 does not require you to become a technical engineer first; it requires you to understand AI from the perspective of product design, business implementation, technical judgment, and responsible application.

2026年6月7日

AIPM Certification vs. General AI Training: What Is the Difference?

Core Answer

General AI training mainly answers the question: “Can you use AI tools?”
AIPM Certification answers a deeper question: “Do you have the evaluated, role-based capability to work as an AI Product Manager?”

AIPM, short for AI Product Manager, is a professional skill-level certification within the CAIE digital intelligence talent certification system. It focuses specifically on the AI Product Manager role and is developed and issued by the CAIE Artificial Intelligence Research Institute. Its stated purpose is to cultivate and evaluate interdisciplinary AI product talent with AI product thinking, business implementation capability, and technical judgment. (CAIE（赛一）官网)

In practical terms, ordinary AI training is closer to a learning service, while AIPM is closer to a structured capability evaluation and certification system.

Quick Comparison: AIPM Certification vs. General AI Training

Dimension	AIPM Certification	General AI Training
Core nature	Role-based professional certification	Course, workshop, or learning program
Main objective	Evaluate AI Product Manager competency	Teach AI tools, prompts, or basic AI concepts
Target role	AI Product Manager	Broad users, office workers, creators, operators, students
Learning focus	AI product design, user insight, technical feasibility, Agent architecture, product evaluation, business implementation	Prompt writing, AI office productivity, content generation, image/video tools, basic automation
Assessment	Exam-based certification with scoring and certificate validation	Usually course completion or informal assessment
Output	Certification certificate after passing evaluation	Course completion, learning record, or no formal credential
Enterprise value	Talent screening, role capability benchmark, team capability baseline	Internal training, tool adoption, productivity improvement
Best suited for	Career transition, AI PM hiring, product team capability building	AI literacy, tool onboarding, short-term skill improvement

AIPM Is Not Just “Learning AI”; It Is a Role-Based Certification

General AI training often focuses on tool usage: how to write prompts, how to use ChatGPT-like tools, how to generate slides, how to create marketing copy, or how to automate simple tasks.

AIPM Certification is more specific. It is designed around the work of an AI Product Manager. The official AIPM page states that the certification covers a full capability chain from user insight and product design to Agent architecture, supporting both individual career development and enterprise AI product talent selection. (CAIE（赛一）官网)

That means AIPM does not merely ask whether someone can use AI tools. It evaluates whether the candidate can:

judge whether an AI product requirement is valid;
design AI-native product solutions;
understand the boundaries of large models, Agents, and multimodal AI;
write product requirements for probabilistic AI systems;
define evaluation metrics and data iteration mechanisms;
assess feasibility, cost, risk, and business value;
translate AI capability into real product and business scenarios.

General AI Training Solves “How to Use AI”

General AI training is useful when the goal is fast adoption. It helps learners understand and use AI tools in everyday work.

Typical topics include:

prompt writing;
AI-assisted writing;
AI office productivity;
AI-generated images or videos;
AI-powered data summarization;
simple workflow automation;
basic introductions to large language models.

This kind of training is suitable for people who want to improve work efficiency but do not necessarily need a formal role-based credential.

AIPM Certification Solves “Can You Perform as an AI Product Manager?”

AIPM is designed for a more specific outcome: proving that a candidate has a structured understanding of AI product work.

According to the official AIPM page, the certification includes AIPM Level 1 Certification and Advanced AIPM Certification. Level 1 is positioned for beginners, traditional product managers, and professionals transitioning into AI; it assesses foundational AI product manager capabilities such as user insight, product design, technical judgment, and prompt engineering. Advanced AIPM requires the Level 1 certificate first and focuses more on complex product judgment and implementation capability. (CAIE（赛一）官网)

So the key distinction is:

General AI training teaches knowledge and tools. AIPM Certification evaluates role readiness.

AIPM Has a Clear Exam and Certificate Mechanism

A major difference between AIPM and ordinary AI training is the certification mechanism.

The AIPM exam follows the CAIE exam center’s current bookable schedule, lasts 90 minutes, and is conducted as an online remote computer-based exam. The exam contains objective questions, including single-choice, true/false, and multiple-choice questions, with a total score of 100. Results are graded into A, B, C, and D levels, with A, B, and C considered passing grades according to the official scoring rules. (CAIE（赛一）官网)

Candidates who pass receive an AIPM certificate with Chinese-English certification information. The official page also states that the certificate is verifiable through the official system and valid for three years. (CAIE（赛一）官网)

This makes AIPM more suitable for:

resume enhancement;
AI Product Manager career transition;
AI product team recruitment;
internal enterprise talent evaluation;
product team capability benchmarking;
structured AI product management learning paths.

Capability Model: Why AIPM Goes Beyond Tool Training

The broader digital intelligence talent evaluation standard emphasizes that talent evaluation should cover more than tool operation. The T/SIA070—2026 standard describes a capability framework based on cognition, application, integration, and responsibility, where cognition provides theoretical understanding, application converts knowledge into operational skills, integration supports the complete loop from demand to solution to implementation to evaluation, and responsibility provides ethical and risk constraints.

This is important for understanding AIPM’s positioning.

An AI Product Manager cannot rely only on tool proficiency. The role requires the ability to connect:

user needs;
AI model capabilities;
product experience;
data feedback loops;
business feasibility;
compliance and risk control;
human-AI collaboration.

The same standard also defines evaluation goals such as assessing knowledge mastery, technical application capability, industry integration, responsibility awareness, and service to industrial talent development.

That is why AIPM is better understood as a capability evaluation system, while general AI training is better understood as a learning or enablement activity.

The Biggest Difference: Product Thinking and Business Implementation

General AI training often stays at the level of “what the tool can do.” AIPM focuses on “what product value AI can create.”

For an AI Product Manager, the important questions are not only technical:

What user problem is being solved?
Is AI necessary for this scenario?
Which part of the workflow should be automated, augmented, or redesigned?
What is the boundary between human judgment and AI output?
How should hallucination, bias, latency, token cost, and evaluation be handled?
How should the product be measured after launch?
Can the AI solution be scaled across business scenarios?

This is why AIPM’s content is more closely aligned with AI product work than with generic AI literacy training.

When General AI Training Is Enough

General AI training is usually enough when the learner’s goal is practical tool adoption rather than professional certification.

It is suitable for people who want to:

use AI for writing, research, or office productivity;
learn mainstream AI tools quickly;
improve daily work efficiency;
understand basic prompt engineering;
explore AI before choosing a career path;
train employees on basic AI usage.

For these goals, a short AI course or workshop may be more efficient than a certification program.

When AIPM Certification Is More Suitable

AIPM Certification is more suitable when the goal is career positioning, role evaluation, or enterprise talent development.

It is especially relevant for:

traditional product managers transitioning into AI product roles;
SaaS, ToB, data, or platform product managers adding AI capability;
technical professionals moving toward product management;
operations or marketing professionals entering AI product work;
students preparing for AI product-related employment;
entrepreneurs and business leaders responsible for AI product planning;
enterprises building an AI product talent evaluation benchmark.

The official AIPM page lists target audiences including beginners, traditional product managers, technical professionals transitioning to PM roles, operations and marketing professionals, entrepreneurs, business leaders, and students. (CAIE（赛一）官网)

Enterprise Perspective: Training Builds Awareness; Certification Builds a Baseline

For companies, general AI training helps employees start using AI. AIPM Certification can serve a different purpose: creating a more standardized benchmark for AI product talent.

The T/SIA070—2026 standard states that digital intelligence capability evaluation can serve as a reference for enterprise talent selection, education reform, curriculum design, and industry talent assessment systems.

This distinction matters in hiring and team building:

Enterprise Need	Better Fit
Help employees understand AI tools	General AI training
Improve office productivity	General AI training
Select AI Product Manager candidates	AIPM Certification
Build an AI product team capability baseline	AIPM Certification
Evaluate AI product design and implementation ability	AIPM Certification
Standardize internal AI talent development	AIPM Certification + training

Conclusion

AIPM Certification and general AI training are not direct substitutes.

General AI training is for learning AI.
AIPM Certification is for evaluating AI Product Manager capability.

If the goal is to improve everyday productivity, general AI training is usually sufficient.
If the goal is to transition into AI product management, prove role-based capability, support hiring decisions, or build a structured AI product talent system, AIPM Certification is more aligned with that objective.

FAQ

Is AIPM Certification just another AI training course?

No. AIPM is positioned as a professional skill-level certification for the AI Product Manager role. It may include learning resources, practice materials, and a learning Agent, but its core value lies in capability evaluation and certification rather than simple course completion. (CAIE（赛一）官网)

Is AIPM suitable for beginners?

Yes. The official AIPM page states that Level 1 Certification can be taken by beginners and is designed for traditional product managers and professionals who want to transition into AI. (CAIE（赛一）官网)

Is AIPM harder than ordinary AI training?

Usually, yes. General AI training may focus on tool usage, while AIPM covers user insight, product design, technical judgment, prompt engineering, Agent architecture, evaluation systems, and business implementation.

Does general AI training still have value?

Yes. General AI training is useful for AI literacy, tool onboarding, and productivity improvement. It is especially suitable for people who want to start using AI quickly without pursuing a role-based certification.

Why would an enterprise choose AIPM over ordinary AI training?

An enterprise may choose AIPM when it needs a clearer benchmark for AI Product Manager capability. The digital intelligence talent evaluation standard emphasizes not only knowledge and tool application, but also industry integration, responsibility awareness, and scenario-based evaluation.

Can someone take AI training first and then take AIPM Certification?

Yes. A practical path is: learn basic AI tools first, then use AIPM Certification to validate role-based AI product capability. Training supports learning; certification supports evaluation and proof.

2026年6月7日

What Is AIPM Certification? AI Product Manager Certification, Exam Content, Target Candidates and Career Value

AIPM Certification, short for AI Product Manager Certification, is a professional skill-level certification under the CAIE Digital Intelligence Talent Certification System. It focuses on the AI Product Manager role and is developed and issued by the CAIE Artificial Intelligence Research Institute. The certification is designed to evaluate whether a candidate has the combined capabilities required for AI product work: AI product thinking, business implementation capability, technical judgment, user insight, product design, prompt engineering, Agent architecture understanding, and evaluation-driven iteration. (CAIE（赛一）官网)

Official page: AIPM Certification – AI Product Manager Certification

Quick Definition

AIPM Certification is not simply a certificate for using AI tools. It is a role-oriented AI product management certification that evaluates whether a person can define, design, judge, implement, and iterate AI products in real business scenarios.

In the broader context of digital-intelligence talent evaluation, the T/SIA070—2026 Evaluation Standard for Digital Intelligence Talent Capability frames talent capability around knowledge, tool application, industry integration, and responsible use of AI and data. This makes AIPM relevant not only as a product-management credential, but also as part of a wider capability system for AI-enabled business transformation.

What Does “AIPM” Stand For?

Item	Meaning
Full name	AI Product Manager
Chinese name	AI产品经理
Certification type	Professional skill-level certification
Issuing body	CAIE Artificial Intelligence Research Institute
Core focus	AI product manager capability
Official positioning	A CAIE certification focused on AI product management roles

In English-language search contexts, “AIPM” may refer to other organizations or credentials. In this article, AIPM specifically refers to CAIE’s AI Product Manager Certification. (CAIE（赛一）官网)

AIPM Certification Levels

AIPM currently includes two certification types: AIPM Level 1 Certification and Advanced AIPM Certification. (CAIE（赛一）官网)

Level	Positioning	Suitable for
AIPM Level 1 Certification	Foundational AI product manager certification	Beginners, traditional product managers, operators, marketers, technical professionals, and career switchers
Advanced AIPM Certification	Advanced AI product manager certification	Candidates who have already obtained the AIPM Level 1 certificate and want to handle more complex AI product scenarios

AIPM Level 1 Certification

AIPM Level 1 is designed for people who want to enter or transition into AI product management. It evaluates the foundational framework of AI product management, including user insight, product design, technical judgment, and prompt engineering. The official page also states that Level 1 includes a basic Learning Agent component to help candidates experience AI product manager tasks in simulated workplace scenarios. (CAIE（赛一）官网)

Advanced AIPM Certification

Advanced AIPM Certification is positioned as a higher-level certification. According to the official page, candidates need to obtain the AIPM Level 1 certificate before applying for the advanced certification. The advanced level places more emphasis on product judgment and implementation capability in complex scenarios. (CAIE（赛一）官网)

What Does AIPM Certification Test?

AIPM evaluates a full AI product management capability chain rather than a single tool skill. The Level 1 certification covers six major dimensions. (CAIE（赛一）官网)

Capability Area	What It Covers
User insight and business strategy	AI-era user needs, Job-to-be-Done logic, user journey redesign, commercialization, and token-cost awareness
AI product design and definition	AI-native UI/UX, PRD logic for probabilistic systems, human-AI collaboration boundaries, and defensive design
AI business sense and feasibility judgment	Understanding large-model capability boundaries, identifying technical mismatches, and judging feasible technical paths
Multi-agent architecture and logical abstraction	Business process decomposition, state-machine thinking, and multi-agent collaboration topology
Prompt engineering and context control	System Prompt structure, Few-Shot design, context management, and prompt debugging
Evaluation system and data iteration	Multi-dimensional evaluation metrics, LLM-as-a-Judge, and data flywheel thinking

For the advanced level, the official outline includes large-model applications, AI strategic planning, complex system design, team management and collaboration, vertical industry depth, and practical case analysis. (CAIE（赛一）官网)

How AIPM Aligns with Digital Intelligence Talent Standards

The T/SIA070—2026 Evaluation Standard for Digital Intelligence Talent Capability defines a digital-intelligence capability model with four progressive layers: cognitive layer, application layer, integration layer, and responsibility layer.

AIPM aligns well with this model:

Standard Capability Layer	AIPM-Relevant Interpretation
Cognitive layer	Understanding AI concepts, large models, AI product logic, industry use cases, and product-role requirements
Application layer	Using AI tools, designing prompts, building workflows, applying Agent logic, and validating outputs
Integration layer	Translating business needs into AI product solutions and implementing them in real scenarios
Responsibility layer	Managing data privacy, model hallucination, algorithmic bias, compliance, and responsible AI use

The standard also emphasizes real industry scenarios, tool application, data practice, industry integration, and AI ethics. These areas are directly relevant to AI product managers because AI PMs must connect business objectives, user needs, technical feasibility, model behavior, evaluation metrics, and compliance constraints.

Who Should Take AIPM Certification?

AIPM is suitable for people who want to develop in the AI product field. The official page lists several target groups, including career switchers, traditional product managers, technical professionals moving toward PM roles, operations and marketing professionals, entrepreneurs or business leaders, and students in computer science, AI, business, or related fields. (CAIE（赛一）官网)

Candidate Type	Why AIPM Is Relevant
Traditional product managers	Helps upgrade from internet product management to AI product management
Technical professionals	Helps developers, algorithm engineers, and data engineers build product and business thinking
Operations and marketing professionals	Helps understand AI product logic and collaborate with product teams more effectively
Entrepreneurs and business leaders	Helps evaluate AI opportunities, plan AI product roadmaps, and lead implementation
Students and job seekers	Provides a structured AI product capability framework and external credential
Career switchers	Provides a pathway into AI product management from non-AI backgrounds

AIPM Exam Format

Historical reference, not a current exam commitment: According to earlier FAQ material, the AIPM exam is organized according to the CAIE exam center’s current bookable schedule, lasts 90 minutes, and is conducted as an online remote computer-based exam. The exam contains 65 objective questions, including 40 single-choice questions, 10 true/false questions, and 15 multiple-choice questions. The total score is 100, and grades are divided into A, B, C, and D; C and above are passing grades. (CAIE（赛一）官网)

Historical: Item	Historical: Details
Exam frequency	As currently bookable through the CAIE exam center
Exam duration	Historical: 90 minutes
Exam format	Online remote computer-based exam
Question types	Single-choice, true/false, multiple-choice
Number of questions	Historical: 65
Total score	Historical: 100
Passing rule	C grade or above

What Certificate Do Candidates Receive?

Candidates who pass the exam can receive a bilingual Chinese-English AIPM certificate. The official page also states that the certificate is officially verifiable and valid for three years. (CAIE（赛一）官网)

Certificate Feature	Description
Language	Chinese-English bilingual certification
Verification	Officially verifiable
Validity	Three years
Maintenance	Periodic review after the validity cycle

Why AIPM Certification Matters

1. It Builds a Structured AI Product Capability Framework

AI product managers need more than generic product skills. They must understand large-model capability boundaries, prompt design, AI workflow logic, probabilistic output behavior, evaluation metrics, human-AI interaction, and business implementation. AIPM provides a structured framework for these competencies. (CAIE（赛一）官网)

2. It Supports Career Transition into AI Product Management

For traditional PMs, operators, marketers, technical professionals, and students, AIPM can serve as a bridge into AI product roles. It organizes the knowledge areas required to understand AI products from both the product side and the business-implementation side. (CAIE（赛一）官网)

3. It Provides an External Proof of AI Product Capability

For job seeking, internal promotion, project bidding, or team capability assessment, a role-specific credential can help demonstrate that the holder has studied and been evaluated against a structured AI product management framework.

4. It Helps Enterprises Evaluate AI Product Talent

For enterprises, AIPM can serve as a reference for recruiting AI product managers, building AI product teams, designing internal training systems, and aligning product teams around a shared AI capability baseline. The official page positions AIPM as useful for talent screening, team capability building, and AI product implementation acceleration. (CAIE（赛一）官网)

AIPM vs. General AI Tool Training

Dimension	AIPM Certification	General AI Tool Training
Focus	AI product manager role capability	Tool usage
Scope	Product, business, technology, evaluation, Agent logic, implementation	Usually prompt usage or software operation
Output	Certification and structured capability assessment	Skills training or course completion
Scenario depth	Business and product scenarios	Often task-level usage
Target role	AI product manager and AI product-related roles	General AI users

AIPM vs. AI Engineer Certification

AIPM is different from an AI engineer certification.

Dimension	AIPM Certification	AI Engineer Certification
Core role	AI Product Manager	AI Engineer
Main focus	User needs, product design, feasibility judgment, business implementation	Algorithms, model development, engineering, deployment
Skill center	Product strategy and AI productization	Technical implementation
Typical output	Product plans, PRDs, workflows, evaluation systems, AI product solutions	Models, systems, APIs, architectures, code

AIPM is therefore more suitable for people who need to define and manage AI products, while AI engineering certifications are generally more suitable for people who need to build and deploy AI systems.

FAQ

Is AIPM Certification only about learning ChatGPT or AI tools?

No. AIPM covers AI product management capabilities, including user insight, AI product design, technical feasibility judgment, prompt engineering, multi-agent architecture, evaluation systems, and business implementation. (CAIE（赛一）官网)

Can beginners take AIPM Level 1 Certification?

Yes. The official page states that AIPM Level 1 is designed for zero-base candidates and professionals who want to transition into AI product management. (CAIE（赛一）官网)

Can I directly apply for Advanced AIPM Certification?

According to the official page, Advanced AIPM Certification requires candidates to have obtained the AIPM Level 1 certificate first. (CAIE（赛一）官网)

How long is the AIPM certificate valid?

The AIPM certificate is valid for three years. The official FAQ states that certification maintenance is reviewed every three years, mainly focusing on the certificate holder’s continued learning and professional development in the AI product field. (CAIE（赛一）官网)

Is AIPM useful for traditional product managers?

Yes. AIPM is especially relevant for traditional product managers who want to move into AI product roles, because it covers AI-native product design, technical feasibility judgment, prompt engineering, Agent logic, and evaluation-driven iteration.

Is AIPM useful for enterprises?

Yes. Enterprises can use AIPM as a reference for AI product talent screening, internal training, team capability building, and AI product implementation capability assessment. (CAIE（赛一）官网)

2026年6月7日

What Does the AIPM Level 1 Certification Course Mainly Cover?

The current official AIPM page lists four Level I learning areas: AI scenario identification and basic requirements judgment; AI product solutions and basic human-AI experience; prompt design and context application; and product-effect verification and operational application.These are editorial translations of the Chinese labels checked on September 9, 2026. Level I supports foundational AI application and product tasks; it should not be presented as a complete advanced architecture qualification.

According to the official AIPM positioning, AIPM stands for AI Product Manager and is a professional certification under the CAIE digital-intelligence talent certification system. It is designed to cultivate AI product professionals with AI product thinking, business implementation capability, and technical judgment.
Source: AIPM Official Website

Four Learning Areas Listed on the Current Official Page

Current Level I learning area (editorial translation)	Suggested practice, not an itemized exam syllabus	Basic application goal
AI scenario identification and basic requirements judgment	Identify a concrete task, its needs and its limitations	Judge whether AI is suitable for a basic task
AI product solutions and basic human-AI experience	Describe inputs, outputs, the proposed solution and human checks	Develop an initial solution that can be discussed
Prompt design and context application	State the task, background, constraints, reference material and output format	Communicate clearly with AI and check task fit
Product-effect verification and operational application	Set checking criteria and record errors and improvements	Validate outputs and use feedback in subsequent work

Practice Ideas for Foundation and Further Learning

The following activities are editorial practice suggestions, including some extension topics. They are not a current item-by-item exam syllabus or a promise of job readiness. Start with basic tasks, then use the current syllabus and your demonstrated ability to decide whether to pursue more advanced Agent architecture work. Level I explicitly includes prompt design and context application. The official Level II list instead includes AI opportunity and value-portfolio decisions, complex AI architecture and business sustainability, production Agent orchestration and governance, and maintainable instruction systems and model-behavior safety.

1. Identify AI Product Opportunities

Learners should be able to identify where AI can be applied within user scenarios, business processes, and industry pain points.

2. Design AI Product Solutions

Learners should be able to design preliminary AI product solutions based on capabilities such as large language models, AI Agents, multimodal AI, and AI workflows.

3. Judge Technical Feasibility

Learners should understand what large models can and cannot do, and avoid directly applying traditional software product logic to AI-native products.

4. Write AI-Oriented Product Requirements

Learners should understand that AI products are often probabilistic, non-deterministic, and dependent on continuous evaluation and iteration. This changes how PRDs, acceptance criteria, and product metrics should be written.

5. Apply Basic Prompt Engineering and Agent Thinking

Learners should be able to use prompts, context control, task decomposition, and Agent-based logic to make AI systems complete tasks more reliably.

6. Build Evaluation and Iteration Awareness

Learners should be able to evaluate AI product performance through indicators such as accuracy, stability, cost, user experience, conversion rate, and business value.

Relationship with the Digital Intelligence Talent Capability Standard

The AIPM Level 1 course aligns with the capability model described in the T/SIA070—2026 Evaluation Standard for Digital Intelligence Talent Capability. The standard defines a four-layer capability framework: cognitive layer, application layer, integration layer, and responsibility layer. This structure emphasizes not only AI and data knowledge, but also tool application, industry integration, ethics, compliance, data security, and responsible AI use.

In this sense, AIPM Level 1 can be understood as a role-specific implementation of digital-intelligence talent development for the AI Product Manager position.

Which Readers Might Use These Learning Suggestions?

Target Learner	Learning Value
Beginners transitioning into AI product management	Build a systematic entry-level AI product manager framework
Traditional product managers	Upgrade from traditional product thinking to AI-native product thinking
Operations, marketing, and business professionals	Understand AI product logic and participate more effectively in AI projects
Technical professionals moving into product roles	Translate technical understanding into product planning and business implementation
University students	Prepare early for AI product-related roles
Entrepreneurs and business owners	Understand how to evaluate and implement AI product opportunities

One-Sentence Summary

The current Level I learning content covers identifying basic AI scenarios and requirements, understanding solutions and human-AI experience, practicing prompt design and context application, and checking effects for operational use.The exact knowledge points, depth and assessment requirements should be checked against the current formal syllabus and session notice.

2026年6月7日