Every concept connects to a practical task.
Nine stages and 36 units. Initial material is openly available; complete practice and save evidence in your learning profile.
01
1-1AI foundations and direction
L1AI capabilities and limitations
About 12 min ↗1-2Common tools and work scenarios
About 15 min ↗1-3Using AI versus building AI applications
About 18 min ↗1-4Plan your learning journey
About 21 min ↗02
2-1Prompting and context
L2Define goals and acceptance criteria
About 12 min ↗2-2Organize material and context
About 15 min ↗2-3Break down tasks and iterate
About 18 min ↗2-4Build reusable task templates
About 21 min ↗03
3-1Verification and reliability
L2Identify unsupported answers
About 12 min ↗3-2Verify material and sources
About 15 min ↗3-3Sensitive material and usage boundaries
About 18 min ↗3-4Evaluate quality, time, and cost
About 21 min ↗04
4-1AI-assisted development
L3Turn requirements into acceptance checks
About 12 min ↗4-2Understand AI-generated code
About 15 min ↗4-3Debugging, tests, and edge cases
About 18 min ↗4-4Version control and change review
About 21 min ↗05
5-1LLM application foundations
L3Model APIs and requests
About 12 min ↗5-2Implement streaming interactions
About 15 min ↗5-3Structured output and validation
About 18 min ↗5-4Errors, usage, and cost control
About 21 min ↗06
6-1RAG project practice
L3Document processing and chunking
About 12 min ↗6-2Retrieval, citations, and answers
About 15 min ↗6-3Evaluate retrieval and answer quality
About 18 min ↗6-4Failure analysis and improvement
About 21 min ↗07
7-1Agents and workflows
L4Tool calling and permissions
About 12 min ↗7-2State and step orchestration
About 15 min ↗7-3Failures, retries, and human handoff
About 18 min ↗7-4Logging and quality evaluation
About 21 min ↗08
8-1System delivery and operations
L4Users and access control
About 12 min ↗8-2Deployment and configuration
About 15 min ↗8-3Performance, monitoring, and cost
About 18 min ↗8-4Updates, operations, and delivery documentation
About 21 min ↗09
9-1