Retrieval-Augmented Generation (RAG) is the cool trick of letting a large language model tap into an external, authoritative knowledge base before it answers, so you get accurate, up-to-date, domain-specific info without retraining the whole thing. It’s all about boosting LLM relevance and keeping costs down.
In this two-hour crash course using Langchain, you’ll spin up a RAG pipeline that makes your AI smarter and tailor-made for your org’s data. Dive into the GitHub repo for hands-on tutorials and code snippets to get started fast.
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