RAG & knowledge systems
Most 'the AI is wrong' bugs are retrieval bugs.
Retrieval-Augmented Generation grounds the model in your data. The hard part isn't calling an LLM — it's getting the right chunks in front of it. Master the retrieval pipeline and you fix the majority of quality complaints.
Key ideas
- 1
RAG = retrieve relevant context, then generate grounded on it. Quality is bounded by retrieval quality.
- 2
The pipeline: ingest → chunk → embed → store → retrieve (often hybrid keyword + vector) → re-rank → assemble context → generate → cite.
- 3
Chunking strategy matters enormously: too big wastes context and buries the answer; too small loses meaning. Chunk on structure, keep metadata.
- 4
Hybrid search (BM25 + vector) + a re-ranker beats pure vector search for most enterprise corpora.
- 5
Evaluate retrieval separately from generation (recall / precision / faithfulness). Add citations so answers are verifiable — essential in a regulated setting.
Where RAG breaks (and how to fix it)
- Bad chunking → answer is split across chunks or buried. Fix: structure-aware chunks + overlap + metadata.
- Pure vector misses exact terms (codes, names). Fix: hybrid search + re-ranking.
- Stale/duplicated data → wrong but confident answers. Fix: ingestion hygiene, dedup, freshness.
- No grounding check → hallucination. Fix: faithfulness evals + require citations.
For a regulated insurer
- Respect data boundaries: index only data the user is allowed to see; enforce access control at retrieval time.
- Keep PII out of embeddings/logs where possible; know where the vector store lives (data residency).
- Citations + source links make answers auditable and build trust with risk/compliance.
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Reading checkpoint
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A RAG system gives a confidently wrong answer. Where do you look FIRST?
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