Retrieval-Augmented Generation
An architectural pattern that retrieves relevant external data to enhance the accuracy of LLM responses.
Retrieval-Augmented Generation is an architectural pattern that improves the quality and accuracy of large language model responses by retrieving verified, external facts to insert into the prompt before the model generates its reply.
How it Works
RAG decouples static model knowledge from active information retrieval. When a query is received, the system searches external sources (like vector databases or SQL engines), extracts the most relevant text snippets or schemas, and merges them into the LLM prompt.
- Semantic Retrieval: Locates context using mathematical semantic match methods or keyword searches.
- Dynamic Integration: Appends the retrieved information directly into the prompt context, grounding the generation step.
Lakehouse & Agentic Relevance
In data lakehouses, RAG is widely used to supply AI engines with real-time schemas, column mappings, and enterprise metrics without retraining models. For agentic analytics, RAG provides the schemas and guidelines needed to construct valid SQL statements. Using Dremio as a RAG query source allows organizations to retrieve governed, up-to-date data from physical tables like Apache Iceberg, ensuring that the model has access to the latest data snapshots.