What is RAG?

Why is RAG important for grounded AI answers?

RAG gives a generative model access to approved, current information at response time, reducing dependence on what the model happened to learn during training.

• Grounds answers in business-controlled sources.
• Updates knowledge without retraining the base model.
• Provides evidence that can be logged or cited.
• Reduces unsupported answers when retrieval and validation are well designed.

How RAG works

• The system transforms the user’s request and conversation context into a retrieval query.
• Search returns passages or structured records from approved, indexed sources.
• A ranking step selects evidence and passes it with source metadata to the language model.
• The model answers within the evidence, while confidence and policy rules decide whether to cite, clarify, or hand off.

RAG in action

A benefits caller asks whether a procedure is covered. The agent retrieves the current plan clause for that member’s policy year and explains it, instead of relying on a general model memory.

How RAG is measured or evaluated

Evaluate with representative conditions and a published definition. Track these measures together:

• retrieval recall for answer-bearing evidence
• ranking precision and source freshness
• answer faithfulness to retrieved evidence
• end-to-end task success and latency

Limitations, risks, and common failure modes

• Relevant evidence may be missing, stale, or inaccessible.
• Retrieval can expose data the caller is not authorized to see.
• The model can still contradict a correct source.
• Long context increases latency in a live call.

RAG vs. fine-tuning

Retrieval-augmented generation supplies current evidence at response time. Fine-tuning changes model behavior or knowledge patterns through training and is harder to update or cite.

What teams should consider when using or implementing RAG

• Enforce access before retrieval, not only before display.
• Store source version and evidence with each answer.
• Test questions with no valid answer.
• Measure retrieval and generation failures separately.