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Keep shoppingRAG with LangChain: Chat with Your Data using LLMs
10 students
RAG with LangChain: Chat with Your Data using LLMs
Build RAG apps with LangChain: embeddings, vector DB (FAISS, Pinecone) & AI chatbots using your data
Created byPratham Uday Chandratre
Last updated 4/2026
English
What you'll learn
- Understand Retrieval Augmented Generation (RAG) and how it powers modern AI applications beyond traditional LLM limitations
- Design and implement complete RAG pipelines using LangChain, including document ingestion, embeddings, retrieval, and response generation
- Build production-ready AI applications that can answer questions from PDFs, documents, and custom knowledge bases
- Master vector databases like FAISS and Pinecone for high-performance semantic search and scalable AI systems
- Apply advanced techniques like text chunking, embedding optimization, and Top-K retrieval to improve RAG accuracy
- Develop a real-world AI PDF chatbot with conversational memory using LangChain and Streamlit
- Integrate embeddings from HuggingFace and local models (Ollama) for flexible and efficient AI solutions
- Understand how real-world companies use RAG systems in production for search, automation, and intelligent assistants
- Gain practical AI engineering skills to build scalable, real-world GenAI applications using LangChain
Explore related topics
Course content
8 sections • 49 lectures • 6h 41m total length
Requirements
- Basic Python knowledge is recommended, but even beginners can follow along with step-by-step guidance
- No prior experience with LangChain or RAG is required — everything is explained from scratch
- Familiarity with AI or Machine Learning concepts is helpful but not mandatory
- A computer with internet connection to install required tools and run AI applications
- Willingness to learn and build real-world AI projects step by step
- No prior experience with vector databases, embeddings, or LLMs is needed
Description
Build AI Chatbots That Understand Your Data — Not Just Generate Text
Most AI courses teach you how to use LLMs.
But in the real world?
1. AI needs to work with your data
2. AI needs to retrieve accurate information
3. 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 data
2. Create a complete RAG pipeline (retrieval + generation)
3. Store and retrieve data using vector databases
4. 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 works
2. Hands-on implementation with LangChain
3. 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
How to build a complete RAG pipeline
How to improve retrieval quality
How to create AI chatbots using your own data
How to design production-ready AI workflows
Who This Course is For
1. Beginners who want to enter Generative AI & LLMs
2. Developers looking to build real-world AI applications
3. Students who want practical experience with LangChain & RAG
4. Anyone who wants to build AI systems beyond simple prompts
Requirements
Basic Python knowledge
Curiosity to build real AI systems
By the End of This Course
You won’t just “know” RAG.
You’ll be able to build AI systems that actually work in real-world scenarios
If You Want to Stay Ahead in AI…
RAG is not optional anymore.
It’s a must-have skill for:
AI Engineers
Data Scientists
Developers
Imagine building AI systems that don’t just generate answers—but understand your data and give accurate responses.
Enroll now and start building AI that understands your data.
Who this course is for:
- Python developers who want to build real-world AI applications using RAG and LangChain
- Data scientists and machine learning engineers looking to integrate LLMs with custom data
- AI enthusiasts who want to understand and implement Retrieval Augmented Generation from scratch
- Developers interested in building document-based chatbots and AI assistants
- Students and professionals aiming to transition into AI engineering and GenAI development
- Anyone who wants to create production-ready AI systems using vector databases, embeddings, and LLMs