RAG
RAG (Retrieval-Augmented Generation) is a technique that enables AI models to look up and cite up-to-date, accurate information from external knowledge bases when answering questions.
How it works
Here's how it works: when a user asks a question, the system first retrieves relevant information from a database (such as company documents or the latest news), then feeds that information along with the question to a large language model (LLM) to generate the answer. This effectively addresses the problems of outdated LLM knowledge and "hallucinations" (made-up facts), making AI responses more reliable and better suited to specific scenarios.
Examples
- 1.A customer service chatbot can query the latest product manuals and return policies in real time to answer user questions.
- 2.A legal research assistant can search a vast database of cases, giving lawyers precise references to statutes and precedents.
- 3.An internal company knowledge base Q&A system, where employees can ask any question about company processes and the system finds supporting evidence from internal documents.
