RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is an architectural pattern that addresses one of the most significant limitations of large language models: their inability to access information beyond their training data. RAG works by combining a retrieval system, typically backed by a vector database, with a generative language model. When a user submits a query, the system first searches a curated knowledge base to find the most relevant documents or passages. These retrieved contexts are then injected into the prompt alongside the original query, allowing the model to generate responses grounded in actual source material.

The RAG pipeline typically consists of several stages: document ingestion, where source materials are chunked and converted into vector embeddings; indexing, where these embeddings are stored in a vector database for efficient similarity search; retrieval, where the most relevant chunks are fetched based on semantic similarity to the query; and generation, where the language model synthesizes a response using both the query and retrieved context.

RAG has become the dominant pattern for enterprise AI applications because it dramatically reduces hallucinations, enables real-time knowledge updates without retraining, and provides citation capabilities that support auditability. Advanced RAG implementations incorporate hybrid search combining dense and sparse retrieval, reranking models to improve precision, query decomposition for complex questions, and guardrails to ensure response quality. Production RAG systems require careful attention to chunking strategies, embedding model selection, retrieval evaluation, and prompt engineering to achieve reliable performance at scale.