Retrieval-augmented generation (RAG) enhances the capabilities of large language models by allowing them to use relevant private data as context for generating accurate responses.
By integrating retrieved documents with a language model's output, RAG systems can more effectively answer specific inquiries that require access to specialized or private information.
Using frameworks like LangChain, organizations can build RAG systems that allow language models to access proprietary data, significantly improving their utility in enterprise applications.
With RAG, individual cases, like a student's query about attendance policies, illustrate how the system can quickly provide valuable information by pulling from relevant sources.
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