RAG connects a language model to a curated retrieval layer. Instead of asking the model to remember everything, the system searches approved sources, sends the relevant passages to the model and asks for an answer grounded in that context.
The RAG pipeline
- Ingest documents from trusted systems.
- Clean, split and label content with source metadata.
- Store embeddings and searchable text.
- Retrieve the best passages for a user question.
- Generate an answer with citations and confidence boundaries.
Where RAG fits
| Use case | Useful sources | Control needed |
|---|---|---|
| Support answers | Docs, tickets, product notes | Escalation for low confidence |
| Policy Q&A | HR, security and finance policies | Version and owner metadata |
| Sales enablement | Case studies, pricing notes, decks | Approved language and caveats |
Retrieval is a product decision
RAG works best when the content model mirrors how people ask questions. Policies, project notes, support issues and contracts need separate retrieval rules because the cost of a wrong answer is different in each area.
Quality checks before deployment
- Test common questions, edge questions and questions that should not be answered.
- Measure whether the retrieved sources are relevant before judging the final answer.
- Separate draft knowledge from approved operational knowledge.
- Add feedback capture so users can mark missing, stale or incorrect sources.