Retrieval-augmented generation (RAG) is an AI architecture that combines a search or retrieval step with a large language model (LLM). When a question is asked, the system first retrieves relevant passages from a trusted knowledge base — such as help articles, product documentation, or past resolved tickets — and then instructs the LLM to generate its response using only that retrieved content.
For support teams, RAG directly addresses the biggest risk of raw LLMs: hallucination. Because the model is anchored to verified source material, answers are more accurate and auditable.
Practical benefits:
- Reduced escalations: Bots can answer complex, product-specific questions correctly without human fallback.
- Faster knowledge updates: Updating source documents automatically refreshes what the AI can answer, without retraining the model.
- Citation support: Systems can surface the exact article used, building customer trust.
RAG quality is commonly evaluated by answer faithfulness (does the response match the source?) and retrieval precision (were the right documents fetched?). It is now a foundational pattern for enterprise support AI deployments.