Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) combines external information retrieval with large language models to produce more factual and up-to-date responses. The concept integrates search, indexing and reranking components with generative models and defines interfaces, evaluation criteria and governance for knowledge-intensive applications. RAG addresses answer accuracy and timeliness.
This block bundles baseline information, context, and relations as a neutral reference in the model.
Definition · Framing · Trade-offs · Examples
What is this view?
This page provides a neutral starting point with core facts, structure context, and immediate relations—independent of learning or decision paths.
Baseline data
Context in the model
Structural placement
Where this block lives in the structure.
No structure path available.
Relations
Connected blocks
Directly linked content elements.