RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is a technique where an AI model retrieves relevant passages from a knowledge base before generating a response, rather than answering purely from what it learned during training. This keeps answers grounded in current, specific, and verifiable information.

Why RAG matters for customer support

A language model trained months or years ago has no knowledge of your current return policy, product catalog, or pricing. RAG closes that gap: when a customer asks a question, the system searches your documents for the most relevant sections, then generates an answer using only that retrieved context. This is what lets an AI agent give accurate, on-brand answers instead of plausible-sounding guesses.

RAG and hallucination

RAG doesn't eliminate the possibility of an AI model generating an incorrect statement, but it substantially reduces it by anchoring the response to retrieved source text rather than the model's general training data alone.