Retrieval-Augmented Generation (RAG)
A framework that improves AI responses by dynamically retrieving facts from an external knowledge base before generating an answer.
Retrieval-Augmented Generation (RAG) is a powerful architectural pattern in AI that combines the reasoning capabilities of a Large Language Model (LLM) with the accuracy of an external knowledge retrieval system.
Standard LLMs generate answers based solely on the fixed data they were trained on, which can lead to outdated information or confident "hallucinations". RAG solves this by introducing a retrieval step:
- When a user asks a question, the system first searches an external database (often a Vector Database) for relevant documents.
- The system retrieves the most pertinent facts or text snippets.
- These facts are appended to the user's prompt as context.
- The LLM reads the context and generates a highly accurate, grounded answer.
RAG is essential for enterprise AI applications because it allows models to answer questions using private, proprietary, or highly up-to-date company data without needing to continuously retrain the underlying model.