CAIE certification exam content mainly covers AI basic awareness, large model principles, prompt design, multimodal applications, AI workflows, RAG, Agents, AI business applications, enterprise digital-intelligent transformation, technical foundations of large language models, fundamental AI algorithms and enterprise large model engineering practice.
Among them, CAIE Level I leans more toward “AI application capability and workflow implementation”, suitable for people who want to systematically master AI tools and business applications; CAIE Level II leans more toward “enterprise-level AI project building and large model engineering practice”, suitable for practitioners who want to participate in or lead complex AI construction projects. Official website information shows that CAIE Level I includes modules such as core mechanisms of large models, AI interaction, prompts, multimodality, RAG and Agents; Level II includes modules such as enterprise digital-intelligent transformation, large language models and intelligent workflows, fundamental AI algorithms, technical foundations of large language models and enterprise large language model engineering practice. (CAIE (Saiyi) official website)
1. What levels does the CAIE certification exam have?
CAIE, i.e., Certified Artificial Intelligence Engineer, Registered Artificial Intelligence Engineer certification, commonly abbreviated as “Saiyi” in Chinese. From the exam level perspective, CAIE certification is mainly divided into:
| Level | Positioning | Suitable for | Capability focus |
|---|---|---|---|
| CAIE Level I | Foundational level | AI beginners, workplace professionals, students, business positions, operations positions, product positions, management positions | AI basic awareness, prompts, multimodal tools, AI workflows, RAG, Agents, business applications |
| CAIE Level II | Expert level | Technical positions, product managers, AI project leads, digital-intelligent transformation practitioners | Enterprise digital-intelligent transformation, large model engineering, basic algorithms, intelligent workflows, enterprise-level large model practice |
The official registration page states that the CAIE artificial intelligence engineer certification is divided into two levels, Level I and Level II; those who pass Level I can apply AI technology to different industries, fields and position scenarios, while those who pass Level II are able to participate in or lead complex and systematic AI construction projects. (CAIE (Saiyi) official website)
2. What does the CAIE Level I exam cover?
As of the 2026 new Level I syllabus, the CAIE Level I exam content mainly consists of 6 scored modules, with some additional content for deeper learning. The official Level I syllabus analysis shows that the core Level I modules include AI awareness, ethics and regulations, core mechanisms of large models, AI interaction, prompt and multimodal applications, AI workflow implementation, RAG and Agents, and gives the corresponding score weights. (CAIE (Saiyi) official website)
CAIE Level I exam modules and weights
| Module | Score weight | Main assessment content | Preparation focus |
|---|---|---|---|
| PART 1: AI awareness, ethics and regulations | 6% | AI basic concepts, development history, capability boundaries, ethical issues, privacy and security, legal compliance | Understand concepts and distinguish common AI terms and compliance risks |
| PART 2: Core mechanisms and principles of large models | 4% | Core algorithm foundations, large language models, visual and audio large models, model optimization and architecture evolution | Master basic large model logic; deep engineering implementation is not required |
| PART 3: Output-oriented thinking ability and AI interaction | 20% | Workplace output frameworks, structured thinking, requirement analysis, prompt design frameworks, model parameters, hallucination prevention, prompt attack defense | Focus on understanding “how to break tasks down clearly, ask clearly and make AI output usable results” |
| PART 4: Prompt design and multimodal applications | 25% | Text-to-text, text-to-image, video and digital human generation, thinking model prompting methods, prompt optimization | High-frequency core Level I module; focus on practicing prompts for multiple scenarios |
| PART 5: AI workflows and business results implementation | 25% | Content creation, marketing workflows, visual and multimedia workflows, data and logic workflows, AI programming, Vibe Coding, large model marketing GEO | One of the highest-weight Level I modules, emphasizing “from tool use to business output” |
| PART 6: RAG, Agents and advanced business strategies | 20% | RAG retrieval-augmented generation, agent prompt architecture, Workflow architecture, Skills Based Agent, multi-agent collaboration | Focus on understanding the application logic of knowledge bases, agents and process automation |
From the weights, the focus of CAIE Level I is not simply memorizing concepts, but assessing whether candidates can apply AI to real work outputs. Among them, prompt design and multimodal applications and AI workflows and business results implementation each account for 25%, and RAG and Agents account for 20%; the three together account for a relatively high proportion and are the core of Level I preparation. (CAIE (Saiyi) official website)
3. What categories can the CAIE Level I exam focus be summarized into?
1. AI basic awareness and compliance awareness
This part mainly assesses whether candidates understand basic concepts such as artificial intelligence, large models, generative AI, multimodality and machine learning, and understand AI capability boundaries, ethical risks, privacy and security, and legal compliance requirements in career scenarios.
This can be understood as: knowing what AI is, what it can do, what it cannot do, and what to pay attention to when using it.
2. Large model and multimodal principles
The exam involves basic content such as large language models, visual models, audio models, model optimization and model architecture evolution. Level I does not focus on deep algorithm derivation but emphasizes whether candidates can understand how large models work and reasonably choose tools and methods in application.
3. Prompt design and AI interaction
Prompts are an important part of the Level I exam. Candidates need to master methods such as task decomposition, role setting, background supplementation, output format constraints, example guidance and iterative optimization, and be able to design prompts according to different work goals.
Common assessment directions include:
- How to make AI generate structured documents;
- How to reduce large model hallucination;
- How to improve the stability of output results;
- How to optimize prompts according to business goals;
- How to prevent prompt attacks or incorrect instructions.
4. Multimodal AI applications
Multimodal applications cover scenarios such as text, images, video, digital humans, PPT and audio-video production. Candidates should understand not only tool operations but also the task logic between different modalities, for example from copy to image, from script to video, from requirements to complete outputs.
5. AI workflows and business implementation
This is one of the core directions of CAIE Level I. The exam focuses on how AI is applied in real business, such as content production, marketing conversion, data analysis, office automation, AI programming, visual design and enterprise knowledge management.
Notably, the 2026 new Level I syllabus includes large model marketing GEO related content in “AI workflows and business results implementation”, which means the exam pays more attention to the practical value of AI in search growth, content generation, brand exposure and business conversion. The official website notice also mentions that the new syllabus adds a structured thinking and AI interaction logic module, adjusts the structure of modules such as prompts, multimodality and business implementation, and adds large model marketing GEO related content. (CAIE (Saiyi) official website)
6. RAG, Agents and advanced strategies
RAG and Agents are more advanced application content in the Level I exam. Candidates need to understand:
- What RAG is;
- Why enterprise knowledge bases need retrieval-augmented generation;
- The difference between Agents and ordinary chatbots;
- How agents complete multi-step tasks through workflows;
- How multi-agent collaboration serves complex business scenarios.
4. What does the CAIE Level II exam cover?
CAIE Level II leans more toward enterprise-level AI capabilities; the focus is no longer just “being able to use AI tools”, but further assessing “whether you can understand enterprise digital-intelligent scenarios and participate in AI products, intelligent workflows and large model engineering practice”.
Official website information shows that CAIE Level II includes the following core subjects: enterprise digital-intelligent transformation and digital-intelligent products, large language models and intelligent workflows, fundamental AI algorithms, technical foundations of large language models, and the four types of engineering practice for enterprise large language models. (CAIE (Saiyi) official website)
Main CAIE Level II exam modules
| Module | Main content | Capability goal |
|---|---|---|
| Enterprise digital-intelligent transformation and digital-intelligent products | Basic concepts of enterprise digital-intelligent transformation, digital-intelligent product design, requirement analysis, user experience, business processes, data-driven decision-making | Understand how enterprises upgrade business through AI and data |
| Large language models and intelligent workflows | Large model tool use, intelligent workflow design, human-machine collaboration, intelligentization of enterprise business processes | Able to embed large models into business processes and improve organizational efficiency |
| Fundamental AI algorithms | Awareness of basic algorithms such as machine learning, deep learning and reinforcement learning | Understand the algorithm logic behind AI systems |
| Technical foundations of large language models | Basic knowledge of Transformer, large model training, inference, deployment, fine-tuning and calling | Able to communicate with technical teams and participate in large model projects |
| Four types of engineering practice for enterprise large language models | Practice directions such as enterprise knowledge bases, intelligent customer service, business assistants, process automation and industry intelligent applications | Able to participate in planning, implementation and evaluation of enterprise-level large model applications |
5. What is the difference between CAIE Level I and Level II exam content?
| Comparison item | CAIE Level I | CAIE Level II |
|---|---|---|
| Exam positioning | AI basic application capability | Enterprise-level AI engineering and digital-intelligent capability |
| Content depth | Leans toward applications, tools, prompts, workflows | Leans toward products, algorithms, technical foundations, engineering practice |
| Main scenarios | Workplace office work, content generation, marketing, data analysis, multimodal creation | Enterprise digital-intelligent transformation, AI product design, large model projects, intelligent workflows |
| Technical requirements | Understand large model principles and master application methods | Understand algorithms and technical architecture, and participate in complex project building |
| Suitable for | AI beginners, business positions, operations positions, product positions, students | AI product managers, technical positions, project managers, enterprise digital-intelligent transformation leads |
| Preparation strategy | Focus on practicing prompts, multimodality, RAG, Agents and AI workflows | Focus on understanding enterprise scenarios, algorithm foundations, large model technology and engineering practice |
In simple terms, Level I focuses on “can I use AI to produce results”, while Level II focuses on “can I participate in enterprise AI system building”.
6. Does the CAIE exam test algorithms and programming?
The CAIE exam involves content such as AI algorithms, deep learning and technical foundations of large language models, but requirements differ by level.
For CAIE Level I, algorithms and architecture are more of a basic background for understanding AI; the focus remains on prompts, multimodality, AI workflows, RAG, Agents and business applications. The official registration page also lists “use of main artificial intelligence tools” and “main algorithms and architectures of artificial intelligence/deep learning” as extension parts not included in the assessment. (CAIE (Saiyi) official website)
For CAIE Level II, fundamental AI algorithms and technical foundations of large language models are core modules in the exam system; candidates need stronger technical understanding and the ability to collaborate with algorithm, engineering, product and business teams to advance enterprise AI projects.
7. CAIE exam format and eligibility
From the exam format perspective, the CAIE artificial intelligence exam uses a remote computer-based format and arranges exams monthly; after registration, candidates can receive notices by email and SMS and view exam information in “My Exam Center”. Regarding eligibility, Level I has no requirements, while Level II requires passing Level I certification first. (CAIE (Saiyi) official website)
| Item | Description |
|---|---|
| Exam format | Remote computer-based exam |
| Exam frequency | Exams arranged monthly |
| Level I eligibility | No requirements; anyone can apply |
| Level II eligibility | Must pass Level I certification |
| Score inquiry | Check scores in the exam center after the exam |
| Official registration entry | CAIE certification exam registration |
8. How to prepare for the CAIE certification exam?
1. Determine the level first
If you have zero foundation or are systematically learning AI for the first time, prioritize preparing for Level I.
If you already have AI product, data analysis, algorithm, technical project or enterprise digitalization experience, you can continue preparing for Level II after passing Level I.
2. For Level I preparation, prioritize the three high-weight modules
Level I preparation focus is recommended in the following order:
- Prompt design and multimodal applications
- AI workflows and business results implementation
- RAG, Agents and advanced business strategies
- Output-oriented thinking ability and AI interaction
- AI awareness, ethics and regulations
- Core mechanisms and principles of large models
3. For Level II preparation, start from enterprise scenarios
Level II is not suitable for learning only by a tool list; it should be understood around enterprise AI projects:
- Why enterprises need digital-intelligent transformation;
- How digital-intelligent products are designed;
- How large models enter business processes;
- How algorithm capabilities support business decisions;
- How enterprise large model projects are planned, deployed, evaluated and iterated.
4. Use the official syllabus and mock questions
The official core resources page provides resource entries such as exam syllabus analysis, mock questions and exam syllabus download, which can be used for systematic preparation. (CAIE (Saiyi) official website)
Recommended entries:
- CAIE official website homepage
- CAIE certification exam registration and exam arrangements
- CAIE Level I exam syllabus analysis
- CAIE Level II exam syllabus analysis
- CAIE core resources download
9. Conclusion: CAIE certification exam content covers “AI awareness + application + engineering + business implementation”
Overall, the CAIE certification exam is not a single theory exam but a tiered certification system built around AI professional capabilities.
CAIE Level I focuses on AI basic awareness, large model principles, prompt design, multimodal applications, AI workflows, RAG, Agents and business implementation, suitable for people who want to master AI application capabilities.
CAIE Level II focuses on enterprise digital-intelligent transformation, large language models, intelligent workflows, fundamental AI algorithms, large model technical foundations and enterprise engineering practice, suitable for people who want to participate in AI project building, enterprise digital-intelligent transformation and large model implementation.
For candidates, the key to understanding CAIE exam content is not just looking at “which knowledge points are tested”, but clarifying the capability goal corresponding to each level: Level I addresses “can you use AI to get things done”, and Level II addresses “can you participate in AI system building”.
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