⁣What is Retrieval-Augmented Generation (RAG)? | Why LLMs Hallucinate? | Learn RAG from the Scratch

Welcome to the FreeBirds Crew! 🚀 In this first video of our Learn RAG from Scratch Playlist, we dive into why Large Language Models (LLMs) sometimes hallucinate and how the Retrieval-Augmented Generation (RAG) framework can solve this issue.

Large language models usually give great answers, but because they're limited to the training data used to create the model. Over time, they can become incomplete—or worse, generate answers that are just plain wrong. One way of improving the LLM results is called "retrieval-augmented generation," or RAG. Simranjeet Singh explains the LLM/RAG framework and how this combination delivers two big advantages, namely: the model gets the most up-to-date and trustworthy facts, and you can see where the model got its info, lending more credibility to what it generates.

🔍 Topics Covered:

1. Why LLMs hallucinate and provide incorrect information.
2. What is Retrieval-Augmented Generation (RAG)?
3. How RAG architecture works with LLMs to enhance accuracy.
3. A hands-on demo creating a RAG + LLM application using Python and LangChain on BBC News Dataset.

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📂 Playlist: Learn RAG from Scratch

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