RAG (retrieval-augmented generation) improves AI business assistants by fetching relevant passages from your docs before the model answers — reducing hallucinations and enabling citations.
When RAG beats fine-tuning
- Policies and manuals change often
- You need source citations for trust
- Multiple departments contribute documents
- You lack labelled fine-tune datasets
Pipeline pieces that decide quality
- Clean ingestion (PDF/HTML/Notion/Drive)
- Chunking strategy matched to doc types
- Embeddings + vector/hybrid search
- Reranking for precision
- Answer with citations and refusal rules
Quality and safety tips
Evaluate with question sets from real support tickets. Block answers when retrieval confidence is low. Keep PII out of indexes unless required — see AI guardrails. Delivery under AI development.
Common failure modes
- Dumping entire PDFs as one chunk
- No refresh when docs update
- Mixing confidential tenants in one index
- Letting the model invent when retrieval is empty
Where agents fit
RAG answers; agents act. Many products combine both — retrieve policy, then create a ticket via tools (what is an AI agent, AI agents).
Prototype RAG on your top 50 questions
Book a call and we will propose an ingestion and eval plan.
