What is RAG? How Retrieval-Augmented Generation Works
Three months ago I was on a call with the founder of a logistics company in Dubai. He had spent a chunk of money on an LLM-powered chatbot trained on his own operations manual, customer emails, and tariff documents. The chatbot was confident, well-spoken, and wrong about his own business — it "remembered" an import duty rate that had changed eighteen months ago, and quoted a customer-facing…
Retrieval-Augmented Generation (RAG) is an architecture that connects a language model to a private corpus without retraining it. This is the key to solving the problem of using language models to answer questions about a company's internal documents. When an LLM is fine-tuned on a company's data, it can hallucinate or say it doesn't know the answer because the model is limited to the data it was trained on.
RAG solves this by retrieving the relevant pieces of data at query time and feeding them into the model during generation. The model then answers based on this retrieved information, without needing to memorize the data itself.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
