Beyond Vector Search - Retrieving the Right Context

Upgrade your prototype RAG into a dependable, production-grade retriever. Combine BM25 and vector search, add a fast re-ranker, and use query reformulation (HyDE) to deliver precise, citable context to your LLM, keeping answers accurate and trustworthy.

You've built a working RAG with vector search. In production, the cracks can appear: a specific figure like "$4.5 billion charge" is missed; a broad "market strategy" query pulls vague, unhelpful chunks. Hallucinations follow not because the model is bad, but because the retriever hands it the wrong context.

This lesson closes the gap between demo and production. You'll engineer retrieval as a pipeline: pair semantic search with BM25 (hybrid), apply a lightweight re-ranker to maximize precision, and strengthen weak queries with HyDE. Finally, you'll return answers with strict source citations so users can verify every claim.