Complete RAG Crash Course With Langchain In 2 Hours

This quick-and-dirty guide (and its companion GitHub repo) shows you how to supercharge any LLM by tapping into external knowledge before it answers. No model retraining required—just plug in your documents, your internal wiki, or whatever knowledge you need.

Retrieval-Augmented Generation (RAG) essentially asks your LLM to fetch relevant facts from a reliable source, then weave them into its reply. The result? More accurate, up-to-date, and domain-specific responses without blowing your budget or your CPU.

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