Retrieval Augmented Generation Rag
Retrieval Augmented Generation (RAG) is a technique that enhances generative AI systems by integrating external knowledge retrieval with language model generation. Rather than relying exclusively on patterns learned during training, RAG systems query a knowledge base or document collection to retrieve relevant information before generating responses. This two-stage approach—retrieval followed by generation—allows AI agents to produce more accurate, current, and grounded outputs.
How RAG Works
A RAG system operates by first receiving a user query, then retrieving semantically similar documents or data from an external knowledge base using retrieval mechanisms such as vector similarity search or keyword matching. The retrieved documents are formatted as context and passed to a generative language model, which produces a response informed by this retrieved information. This allows the system to reference specific sources and provide answers grounded in actual data rather than relying solely on training data.
Key Advantages
RAG addresses several limitations of standalone generative models. It enables systems to work with knowledge updated after training, reducing the risk of hallucinations by constraining generation to retrieved facts, and allows transparency through source attribution. This makes RAG particularly valuable for domains requiring accuracy and verifiability, such as question-answering systems, customer support, and technical documentation.
Source Notes
- 2026-04-07: Karpathy’s LLM Wiki: Watch Me Build a Knowledge Base From
- 2026-04-08: Next Evolution of Retrieval-Augmented Generation
- 2026-04-10: Karpathys LLM Wiki Beyond RAG for Persistent Knowledge Bases · ▶ source