RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is the design pattern of giving an LLM access to a knowledge base — your docs, past tickets, product info, company data — at inference time, so its responses are grounded in that real source data rather than just what the model learned during training.

The flow looks like:

1. Query arrives. "How do I reset my password?"
2. Retrieval. System searches the knowledge base for relevant documents (typically using vector similarity + sometimes keyword search).
3. Context assembly. Top N relevant docs get included in the prompt to the LLM.
4. Generation. LLM produces a response grounded in those retrieved docs, with citations.
5. Response. Customer gets an answer based on the actual product docs, not the model's general training.

RAG is critical for AI automation because it solves the two biggest problems with naked LLMs:

• Hallucination. Without grounding, LLMs confidently make up plausible-sounding wrong answers. With RAG, they can only answer from real sources.
• Freshness. LLM training data has a cutoff date. RAG lets you give the model up-to-date information about your business.

Most production AI automation uses RAG somewhere — customer support agents grounded in product docs, sales agents grounded in past deal history, legal agents grounded in precedent libraries.