Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) is a technique that enhances large language model responses by retrieving relevant information from external knowledge bases before generating answers. Rather than relying solely on what the model learned during training, RAG gives it access to current, specific, and verifiable information at inference time.

The pattern is straightforward: when a user asks a question, the system first searches a knowledge base (documents, databases, web pages) for relevant context, then passes that context along with the question to the LLM. The model generates its response grounded in the retrieved information. This dramatically reduces hallucination—the tendency of LLMs to generate plausible-sounding but incorrect information—and ensures responses reflect current data.

RAG has become foundational to enterprise AI deployments. Organizations use it to build AI systems that answer questions about their own documents, products, policies, and data without the cost and complexity of fine-tuning models on proprietary information. Customer support agents, internal knowledge assistants, and research tools all commonly use RAG architectures.

The technique is particularly important for AI agents. An agent that can retrieve information from specific knowledge bases can operate with far greater accuracy and relevance than one relying solely on its training data. Combined with the Model Context Protocol, RAG-enabled agents can dynamically access and incorporate information from multiple sources as they work.

As context windows for LLMs expand to 100k–200k tokens, the relationship between RAG and long-context models is evolving. Longer context windows allow models to process entire documents directly, but RAG remains essential for searching across large knowledge bases and ensuring relevance at scale.

An emerging alternative to RAG is the Recursive Language Model (RLM) architecture, which takes a fundamentally different approach to grounding AI in external knowledge. Where RAG retrieves static document chunks and passes them as context, RLMs use recursive self-referencing and iterative refinement to build deeper understanding of source material. RLMs can potentially maintain more coherent reasoning across complex, multi-step queries because the model re-engages with its own intermediate outputs rather than relying on a single retrieval pass. For applications requiring nuanced synthesis across many sources—research, legal analysis, complex technical documentation—RLMs may offer advantages over traditional RAG pipelines, though RAG remains the more mature and widely deployed pattern.