Azure AI Search: The “R” in Retrieval Augmented Generation

Retrieval-Augmented Generation (RAG) is a technique that significantly enhances AI’s ability to answer questions accurately by retrieving relevant information from external documents and integrating it into responses. The retrieval component serves as a bridge between AI and specific data sources, pulling in the most relevant, up-to-date information to address each question. By grounding AI-generated answers in precise, factual data, RAG overcomes the limitations of language models that may lack current or specialized information. High-quality retrieval is crucial for RAG to succeed, as it directly impacts the accuracy and relevance of the AI’s responses.