Glossary

Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) is an architectural framework that enhances large language model outputs by grounding them in external, private, or real-time data sources. By querying a vector database or document store before generation, the system provides the model with relevant context, significantly reducing hallucinations and increasing the factual accuracy of enterprise AI responses.

In enterprise environments, RAG is critical because base models lack awareness of proprietary data, internal policies, and evolving business states. Relying solely on pre-trained model weights leads to outdated or generic outputs that fail to meet governance requirements. By decoupling the knowledge base from the model parameters, RAG allows organizations to maintain a single source of truth, update information without retraining models, and provide verifiable citations, which are essential for auditability and compliance in high-stakes automated workflows.

Implementing RAG requires a robust pipeline involving document ingestion, chunking, embedding generation, and semantic search. Practitioners must focus on retrieval quality, often employing techniques like hybrid search, re-ranking, and query expansion to ensure the most pertinent data reaches the model. Furthermore, developers must monitor the context window constraints and the potential for noise in retrieved documents. Effective governance involves evaluating retrieval precision and ensuring that access controls are enforced at the data layer to prevent unauthorized information disclosure.

Last updated: 2026-08-25