RAG Optimization Playbook
A practical blueprint for improving retrieval quality, reranking and source attribution in RAG systems in plain language.
RAG systems live or die by retrieval quality. I start with query analysis then tune chunking and embeddings before touching the generator. If the retriever is bad, the generator just makes the wrong answer sound confident. That is not the vibe.
A hybrid retriever (BM25 + dense) with reranking boosts recall and precision. I prefer a lightweight cross encoder when latency allows and fall back to score fusion when it does not.
For production, I enforce citation grounding and evaluation loops. Answer quality improves when you track attribution coverage and feedback signals, not when you add more prompt magic.
If you are not logging false positives, you are just collecting nice looking metrics. Ask me how I know.
- Chunk by semantic boundaries, not fixed tokens.
- Use metadata filters to shrink the candidate pool.
- Rerank for high precision and cite evidence spans.
- Measure latency and coverage on every release.
- Do not ship without a negative set unless you enjoy support tickets.