What is RAG (retrieval augmented generation)?
Retrieval-Augmented Generation (RAG) is the technique of pairing a language model with a vector database so the model answers using passages retrieved from your content rather than from what it memorized during training.
Why it matters
Without retrieval, an LLM hallucinates plausible-sounding facts. With retrieval, the model is constrained to summarize and reformulate passages from a corpus you control. For customer support, this is the difference between a useful AI assistant and an embarrassing one.
The pipeline
Embed the query into a vector. Search your vector index for the top K nearest passages. Inject those passages into the prompt. Ask the model to answer using only the provided context. Optionally, require citations to specific passage IDs.
Customer URLs get crawled, chunked, embedded, and indexed in our self-hosted retrieval stack. Every AI reply is grounded in retrieved chunks, with a confidence threshold that hands off to a human when retrieval is weak. Each answer cites the passage it used.
See it working on message.com.
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