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Artificial Intelligence
RAG (Retrieval-Augmented Generation)
RAG combines a retrieval system with an LLM to ground responses in external knowledge.
Definition
Retrieval-Augmented Generation (RAG) is an AI technique that combines a retrieval system (search) with a generative LLM to produce more accurate, factual, and up-to-date responses. In RAG: (1) User asks a question, (2) System retrieves relevant documents from a knowledge base (vector database), (3) Retrieved documents are added to the LLM's context, (4) LLM generates a response grounded in the retrieved context. RAG is widely used for enterprise AI applications — chatbots, document Q&A, knowledge management. Harch Corp provides GPU infrastructure for RAG pipelines.
Related Keywords
ragretrieval augmented generationrag llmvector database rag
Related Terms
Large Language Model (LLM)
An LLM is an AI model trained on massive text data to understand and generate human language.
AI Inference
AI inference is the process of using a trained model to make predictions on new data.
Vector Database
A vector database stores and queries high-dimensional vectors for AI similarity search.