Build · Intermediate
LLM engineering and RAG
Prompting, structured output and retrieval-augmented generation for applications grounded in your own data.
Why it matters
Most production AI work is engineering around a model: getting the right context in, getting reliable structure out, and measuring the result.
Key ideas
- Prompt design and system messages
- Structured output
- Chunking and vector search
- Retrieval-augmented generation
- Long-context limits
You will be able to
- Build a RAG pipeline
- Cite retrieved evidence
- Diagnose retrieval failures