What Is RAG?

Retrieval-augmented generation, commonly called RAG, combines information retrieval with generative AI. Instead of asking a language model to answer using only its internal training, the application retrieves relevant information from a controlled knowledge source.

Typical RAG Flow

1. Collect and prepare source documents.
2. Split content into useful chunks.
3. Create searchable representations.
4. Retrieve relevant information for a user query.
5. Provide the retrieved context to the model.
6. Generate and validate the response.

Quality Depends on Retrieval

A powerful model cannot compensate for poor retrieval. Document quality, chunking, metadata, ranking, access controls, and evaluation all affect the final answer.

RAG is particularly useful for internal knowledge assistants, documentation search, support systems, and domain-specific applications.