Understanding RAG for AI Applications

Retrieval-Augmented Generation (RAG) combines a language model with a searchable knowledge base. The application retrieves relevant sources before generating an answer.

How RAG works

1. Split and index your documents.
2. Retrieve the most relevant passages for a user question.
3. Provide those passages as context to the model.
4. Return an answer with source references.

RAG is useful when answers must reflect private, changing, or domain-specific information.