Retrieval-Augmented Generation (RAG) is defined as?

Retrieval-Augmented Generation (RAG)enhances Large Language Models (LLMs) by integratingexternal, up-to-date, or proprietary informationinto the generation pipeline—allowing the model tofetch relevant factsfrom a trusted knowledge source at query time.

Though RAG is not defined directly in the IAPP documents, it is a widely recognized technique in AI governance for ensuringmore accurate and contextually grounded outputs, especially inregulated or high-stakes environmentswhere hallucinations are a concern.

B, C, and Ddescribe optimization or bias mitigation—not the core function of RAG.