11 · Enterprise RAG Pipeline

This is an enterprise RAG pipeline that takes a user question and returns an accurate, grounded answer with citations. It shows every step from query rewriting to retrieval, reranking and generation.

How it works

1. 1Rewrite the query (expand, decompose) and run hybrid search (vector + BM25).
2. 2Apply metadata filters (access control, date, type) and retrieve the top-K chunks.
3. 3Re-rank with a cross-encoder for relevance and expand context with parent/neighbouring chunks.
4. 4Generate the answer with the LLM (Bedrock) using the high-quality context.
5. 5Return the final answer with citations to source links.

Key points

• Offline: documents are chunked, embedded and stored with metadata.
• Online: query rewriting → hybrid search → filter → rerank → expand → generate.
• Observability and evaluation run at every step (LangSmith).
• Key principles: retrieve fewer, better chunks; filter early; rerank for relevance.

Why it matters

A well-built RAG pipeline is what makes enterprise AI answers accurate, up-to-date, traceable and secure — the difference between a demo and production.

Frequently asked questions

What is hybrid search?

Combining keyword (BM25) and vector (semantic) search for higher recall than either alone.

Why re-rank?

A cross-encoder scores query+document together, picking the truly most relevant chunks before the LLM sees them.