By RON-LY Solutions Engineering Team · Published August 28, 2026
Short answer
Retrieval-Augmented Generation (RAG) retrieves relevant information from an organization’s own knowledge base at the moment of a query, then feeds it to a language model to generate an answer grounded in that specific information — rather than relying only on what a public model was trained on.
Why it matters
A general-purpose AI model doesn’t know an organization’s internal documents, policies, or data. RAG closes that gap without the cost and complexity of retraining a model, and it keeps sensitive data out of the model’s training set entirely.
Key architecture elements
- Knowledge base — the organization’s documents, indexed and chunked for retrieval
- Vector search — finding the most relevant pieces of content for a given query
- The language model — public API or private/local, depending on data sensitivity
- Orchestration — combining retrieved content with the query before generating a response
Common mistakes
- Poor document chunking that returns irrelevant context
- No access control on what the retrieval layer can surface to which users
- Assuming RAG eliminates the need to verify AI-generated answers