What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) is a technique where an AI system first searches your own documents for the passages relevant to a question, then writes its answer using what it found.
The simple explanation.
A language model knows a lot about the world and nothing about your business. Retrieval fixes that without retraining anything. When a question comes in, the system first searches your own material, price lists, policies, past tickets, product sheets, and pulls out the handful of passages that look relevant. Those passages are handed to the model along with the question. The model then answers using what is in front of it, the way a staff member checks the manual before replying.
Why it beats training your own model.
Training a model on your data is slow, costly, and out of date the moment a price changes. With retrieval, updating the agent means updating the document. Change the return policy this morning and the agent quotes the new policy this afternoon. You can also trace which passage produced an answer, which matters when a customer disputes what they were told. For almost every SME use case, retrieval is the right tool and custom training is not.
What makes it work well.
Two things decide whether retrieval feels reliable. First, the quality of the source material: contradictory or outdated documents produce contradictory answers, so the cleanup is part of the project. Second, the search itself, which usually combines meaning based matching with keyword matching, so that both a vague complaint and a specific order number find the right thing. Get those right and the answers stay grounded. Get them wrong and the model fills the gap on its own.
The Voltade take
Retrieval is how we ground agents in your actual documents rather than a model's general knowledge. In practice that means an intake step where your policies, price lists and past conversations get organised and made searchable, then a strict instruction to answer only from what was retrieved and hand over to a person when nothing relevant comes back.
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