In just two hours, this crash course dives into Retrieval-Augmented Generation (RAG) using LangChain to boost your LLM’s smarts by tapping into external knowledge bases—no retraining required. You’ll see how hooking your model up to a dedicated info source (like internal docs or specialized datasets) makes responses more accurate, relevant, and context-aware.
Plus, it’s cost-effective and super flexible, so you can adapt it to any domain—from company wikis to research archives—keeping your AI answers sharp and up-to-date. Get the full code and step-by-step guides on GitHub: krishnaik06/RAG-Tutorials
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