RAG Development Guide for Internal Knowledge

RAG (retrieval-augmented generation) lets an assistant answer from your documents instead of guessing from model memory. ## When RAG is the right tool - Internal policies, SOPs, and product docs change often - You need citations or source links - Access control matters (not everyone should see everything) - Fine-tuning would be slower and harder to update ## Build checklist 1. Collect sources (Drive, Confluence, PDFs, Notion) 2. Clean and chunk with structure in mind (headings, tables, procedures) 3. Embed and index with metadata (team, product, sensitivity) 4. Retrieve with hybrid search when keyword precision matters 5. Generate with citations and refusal behaviour 6. Evaluate with a golden question set monthly ## Failure modes - Chunks that split procedures mid-step - Stale indexes after docs change - Over-retrieval that confuses the model - No permissions layer on sensitive content ## Production extras Audit logs, feedback buttons ("was this helpful"), and a path for subject-matter experts to correct answers. ## Next step If your team hunts the same answers every week, a knowledge assistant pilot is usually faster than another wiki restructure.