RAG = Retrieval-Augmented Generation. Pull relevant context, then generate.

What you're seeing

RAG: instead of relying solely on the LLM's parametric knowledge, retrieve relevant context from a knowledge base. Reduces hallucination; lets you use up-to-date info.

Stages: embed query → vector search top-K docs → format docs into prompt → LLM generates with context. Quality depends on retrieval (recall) more than generation in most pipelines.

★ KEY TAKEAWAY
RAG: embed query → vector search → augment prompt with docs → generate. The standard pattern for grounded LLM apps.
▶ WHAT TO TRY
  • Step through the 6 phases.
  • Quality of retrieval (step 3) usually matters more than generation (step 5).
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