Retrieval-Augmented Generation (RAG) 101
How AI systems combine language models with external knowledge sources.
The problem it solves
A model's knowledge is frozen at training time and limited to what it memorized. RAG fixes both limits by retrieving relevant documents from an external source at query time and feeding them into the prompt as grounding context.
How it works
A query is converted into an embedding, matched against a vector database of pre-indexed document chunks, and the top matches are inserted into the model's context window alongside the original question.
Why agents rely on it
For an AI agent, RAG (and its live-data cousin, real-time web retrieval) is what keeps answers current and specific to an organization's own data, rather than relying purely on what the base model happened to learn from public training data.
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