RAG stands for Retrieval-Augmented Generation, commonly called Retrieval-Augmented Generation in Chinese. Its core is not to have the model remember all the information, but to first search for relevant content in external knowledge bases before answering, and then have the large model generate answers based on that information.
What problem does it solve?
Ordinary large models rely only on knowledge learned during training and current inputs, and are prone to making mistakes when encountering company policies, product documents, contract terms, or the latest materials. RAG separates "finding information" from "writing answers": the retrieval system is responsible for finding relevant document fragments, while the model organizes, explains, and expresses them.
The basic process of RAG
- Break the document into smaller segments, retaining metadata such as title, source, and permissions.
- Use the Embedding model to convert questions and document fragments into vectors.
- Use vector databases or search engines to find the most relevant segments.
- Insert the clip into the prompt and let the model answer based on evidence.
- If necessary, return to the source for manual verification.
RAG does not mean no hallucinations
RAG can only reduce hallucinations and does not automatically eliminate them. If the search is incomplete, the slices are too poor, permission filtering errors, the clips are outdated, or the prompt requirements are unclear, all these will cause the answer to be off-topic. Many knowledge base Q&A failures are not due to poor models, but because retrieval and document governance are not done well.
What scenarios are they suitable for?
RAG is suitable for enterprise knowledge bases, customer service Q&A, policy searches, paper material organization, and product manual assistants. It is not suitable for tasks that require models to be created out of thin air, complex mathematical reasoning, or lack reliable data sources. To decide whether a project should use RAG, you might ask: Does the answer have to come from a batch of traceable external data? If so, RAG is usually more stable than a simple prompt.
