<ul data-eligibleForWebStory="true"><li>Retrieval-Augmented Generation (RAG) combines retrieval system with generative language model.</li><li>External data helps model generate accurate, context-aware answers without changing weights.</li><li>Steps to develop RAG strategy include document preparation, embedding creation, and answer generation.</li><li>Use cases span customer support, research, legal, education, and enterprise knowledge sectors.</li><li>Improving RAG code involves upgrading models, optimizing search, managing context, and monitoring output.</li></ul>

How to Develop AI with Retrieval-Augmented Generation (RAG)

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