RAG
RAG (Retrieval-Augmented Generation) is an AI technique that combines a large language model's ability to generate text with a search system's ability to find relevant, up-to-date information from a specific database.
How it works
It works by first retrieving pertinent documents or data from a source (like a vector database) based on a user's query, and then feeding that information as context to the language model to generate a more accurate and fact-based answer. This matters because it helps reduce AI 'hallucinations' by grounding responses in verified information, making AI systems more reliable and useful for knowledge-intensive tasks.
Examples
- 1.A customer service chatbot that pulls the latest product manuals and support articles to answer specific technical questions.
- 2.An AI research assistant that searches through a company's private documents and recent industry reports to summarize findings on a new topic.
- 3.A legal tool that retrieves relevant case law and statutes to help a lawyer draft a memo or contract clause.
