Retrieval-Augmented Generation (RAG)<p><strong>Retrieval-Augmented Generation (RAG)</strong> combines information retrieval with <a href="https://en.wikipedia.org/wiki/Large_language_model" target="_blank" rel="noopener">large language models (LLMs)</a> so responses can be grounded in external documents, knowledge bases, databases, or enterprise content.</p> <p>RAG is useful for technical documentation, support knowledge, policy search, research synthesis, and internal knowledge-management workflows because it can make AI output more traceable to source material.</p> <p><strong>Additional Resources</strong>:</p> <ul> <li><a href="https://en.wikipedia.org/wiki/Retrieval-augmented_generation" target="_blank" rel="noopener">Retrieval-augmented generation - Wikipedia</a></li> <li><a href="https://cloud.google.com/use-cases/retrieval-augmented-generation" target="_blank" rel="noopener">Google Cloud: What is RAG?</a></li> </ul>