1. Definition: What is RAG
RAG stands for Retrieval-Augmented Generation, that is, retrieval-augmented generation technology. It is an AI model architecture that combines large language models (LLMs) with external knowledge bases, enabling the model to retrieve relevant information before generating responses.
2. The workflow
- indexing stage of RAG: convert text or documents into vector representations and store them in the vector database;
- Retrieval stage: When the user asks a question, the system also converts the query into a vector and retrieves the relevant content in the database.
- Enhancement stage: splicing the retrieved information into the prompt as the generated context;
- Generation Stage: The language model generates responses based on the raw training data and the retrieved context.
3. The main advantages of RAG
- reduce "hallucinations": external reliable information can be cited, and the answers generated by the model are more credible;
- Instant knowledge updates: Quickly supplement new information without frequent retraining;
- Support customized applications: It can be connected to the company's internal document library to provide high-accuracy output for domain tasks.
4. Typical application
- scenariosEnterprise customer service robots provide accurate policy answers by accessing the company's document system; The
- knowledge quiz system calls industry reports, regulations, user manuals and other data to generate answers;
- The education platform retrieves the latest information and generates accurate answers.
5. Conclusion
RAG is a generation method that allows LLMs to supplement information in real time using external knowledge bases, making answers more accurate, traceable, and adaptable. It has become an important technology solution for enterprises to customize AI services.
