What is Retrieval Augmented Generation (RAG) | What is LangChain | GenAI Full Course #8

Ever wished you could ask ChatGPT questions about YOUR own private documents?
Large Language Models (LLMs) are incredibly powerful, but they have a major limitation: they don't know anything about your specific data and can often "hallucinate" or make things up. The solution? Retrieval Augmented Generation (RAG)*

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Document for RAG System: https://certain-mechanic-42c.notion.site/RAG-System-23c3a78e0e22801caa04d16f95df1825

DSA PDF: https://drive.google.com/file/d/1QAOLVDXTH517pIwzJnsJ8TKYnDtXDzoj/view?usp=sharing

In this video, I'll break down exactly what RAG is and how you can use it to build smarter, fact-based AI applications. We'll explore the essential tools that make this possible:
- LangChain:The ultimate framework for orchestrating complex LLM workflows.
- Pinecone: The high-performance vector database built for storing and querying your data's meaning.

But we won't stop at theory! I'll walk you through building a complete project from scratch. By the end, you'll have an app that can ingest any document and answer your questions with incredible accuracy, citing the information directly from the source.

💡 **This video is for you if:**
- You're a developer looking to build practical AI applications.
- You're tired of LLM hallucinations and want more control.
- You've heard the buzzwords RAG, LangChain, and Pinecone but want a clear explanation.
- You want to build a "Chat with your PDF" style application.

Видео What is Retrieval Augmented Generation (RAG) | What is LangChain | GenAI Full Course #8 канала Rohit Negi