Retrieval is the product
A chatbot that “knows your docs” fails in the retrieval layer long before the model does. We start with source of truth, chunk sizes that match how people ask questions, and a citation UI that makes misses obvious.
We never ship a RAG demo without an eval set of 40–80 real questions from support tickets. If the answer cannot point at a chunk, it is a refusal, not a guess.
Stack we reach for
- Embeddings + hybrid search (keyword + vector) for SKUs and policy language.
- Reranking on the top 20 hits before the prompt is built.
- Structured output so the UI can render sources next to the answer.
Takeaway
If you cannot audit why the model said something, you do not have a knowledge assistant. You have a very confident intern.