RAG (Retrieval-Augmented Generation) is a technique that combines information retrieval from a knowledge base with text generation using
artificial intelligence. It's like giving an
AI model access to a custom library before asking it to answer questions.
Imagine you have a virtual assistant with access to all the documents in your company. When you ask it a question, it first searches these documents for relevant information and then uses this information to generate an accurate and contextualized response.
This technique addresses one of the biggest challenges of language models: "
hallucinations" or the generation of incorrect information. By grounding responses in real documents, RAG ensures the information is accurate and verifiable. It also allows the system to stay updated without the need to retrain the entire model.
RAG is implemented across various fields: enterprise chatbots, customer service systems, technical documentation, and even in the medical domain, where it helps quickly reference patient records and updated medical literature.