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RAG with LangChain Chat with Your Data using LLMs

Name: RAG with LangChain Chat with Your Data using LLMs
Author: Baturi

Free Download RAG with LangChain Chat with Your Data using LLMs
Published 4/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 6h 41m | Size: 6.83 GB
Build RAG apps with LangChain: embeddings, vector DB (FAISS, Pinecone) & AI chatbots using your data

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

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

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

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