Large Language Models: From Prompts to Practice
Understand tokens and context, build reusable prompts, work with source documents and JSON, and evaluate a prompt portfolio honestly.
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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
Primary reading
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LLM Engineering: RAG, Evals & Multimodal Apps
Build a transparent retrieval baseline, validate citations and access, design multimodal workflows, and evaluate an evidence-based tutor prototype.
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