RAG that doesn't hallucinate: grounding your data

Retrieval-augmented generation (RAG) is the workhorse of applied AI: instead of hoping a model memorized your domain, you retrieve the relevant facts at query time and ask the model to answer from those facts. Done well, it slashes hallucination and keeps answers current. Done carelessly, it just launders bad retrieval into confident prose.

Retrieval is the hard part, not generation

Most RAG failures are retrieval failures. The model answered faithfully, from the wrong context. Where teams win or lose:

• Chunking. Too big and you bury the signal; too small and you lose it. Chunk on semantic boundaries, not arbitrary character counts.
• Embeddings and search. Pure vector search misses exact terms; keyword search misses meaning. Hybrid retrieval plus a reranking step consistently beats either alone.
• Freshness. Your knowledge base changes. If your index doesn’t, your answers rot.
• Metadata and filtering. Scoping retrieval by tenant, date, or permission is often the difference between “helpful” and “data leak.”

Grounding and citations

Ask the model to answer only from retrieved context, and to cite it. Citations aren’t decoration. They make the system auditable and give users a way to verify. When the context doesn’t contain the answer, the correct output is “I don’t know,” not a plausible guess.

Measure it like a search system

Evaluate retrieval quality (did we fetch the right passages?) separately from generation quality (did we use them faithfully?). You need both to improve either. A small, curated eval set catches regressions that a demo never will.

RAG isn’t magic. It’s a well-built search system with a language model on top. The teams that treat it that way are the ones whose AI features hold up.