RAG & Retrieval

Retrieval is where most RAG systems win or lose

Retrieval-augmented generation (RAG) is the most common way to make an LLM useful over your own data — but a working demo and a production system are very different things. The gap is almost always retrieval quality: getting the right context in front of the model, consistently, at acceptable cost and latency.

This section covers the parts that actually matter in production: chunking strategies and why the naive approach breaks, retrieval and reranking, grounding responses so they cite sources instead of hallucinating, and — critically — how to evaluate a RAG system so you catch regressions before your users do.

Topics

• Building a production RAG pipeline: chunking, retrieval, and reranking
• Reducing hallucinations with grounding and citations
• RAG evaluation: the metrics that catch regressions

Written from hands-on engineering work, for teams who need retrieval that holds up.