RAG with LangChain: Chat with Your Data using LLMs

About this course

Build AI Chatbots That Understand Your Data — Not Just Generate TextMost AI courses teach you how to use LLMs.

But in the real world?1. AI needs to work with your data2. AI needs to retrieve accurate information3. AI needs to avoid hallucinations

That’s where RAG (Retrieval-Augmented Generation) comes in. In this course, you won’t just learn theory…You will build real-world AI applications step-by-step using: LangChain LLMs (Large Language Models) Embeddings & Vector Databases FAISS & Pinecone End-to-End RAG Pipeline Streamlit UI for your chatbot

What You Will Build

By the end of this course, you will be able to:1. Build an AI chatbot that can chat with your own data2. Create a complete RAG pipeline (retrieval + generation)3. Store and retrieve data using vector databases4. Develop real-world AI applications used in industry

This is not a toy project.

This is exactly how modern AI systems are built.

Why This Course is Different

Most courses either:- Teach only theory- Or only show disconnected code

This course is designed to give you:1. Clear understanding of how RAG actually works2. Hands-on implementation with LangChain3. Real-world use cases (PDF chatbot, knowledge base AI)4. Practical insights to avoid common mistakes

What You Will Learn What is RAG and why LLMs alone are not enough

How embeddings capture semantic meaning

How vector databases like FAISS & Pinecone work

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