RAG Explained for Builders

Retrieval-Augmented Generation (RAG) combines search over your documents with a language model so answers ground in your data instead of only static training weights.

Moving parts

1. Chunking — Split docs into overlapping segments sized for embedding models.
2. Embeddings — Turn chunks into vectors stored in a vector database or hybrid search index.
3. Retrieval — On each query, fetch the top-k relevant chunks.
4. Generation — Prompt the model with those chunks plus user instructions.

Why not paste whole PDFs?

Context windows are finite; retrieval keeps prompts focused and reduces hallucination risk when citations matter.

Failure modes

Stale indexes, bad chunk boundaries, and permissive prompting without verification — treat RAG as assistive, not authoritative for regulated domains without human review.