Retrieval-Augmented Generation is a technique where an LLM queries external knowledge bases before generating responses. Instead of relying solely on knowledge baked into the model during training, RAG dynamically fetches relevant information at inference time. This solves multiple problems simultaneously. First, it reduces hallucination.
If the model can retrieve factual information from a reliable source, it's less likely to invent false details. Second, it adds real-time data access. A model trained in 2023 doesn't know about 2024 events. RAG can retrieve current information. Third, it separates knowledge from model weights. You don't need to retrain the model every time facts change. You update the knowledge base.
This architecture works like this: a user asks a question, the system retrieves relevant documents from a knowledge base, the LLM reads those documents and generates an answer informed by them. The retrieval step is everything. Bad retrieval means the LLM sees irrelevant information, which degrades output quality. Good retrieval means the model has the right context to answer accurately.
RAG is being deployed across customer service, research, question-answering, and internal knowledge management. It's the practical bridge between general AI and domain-specific reliability.