Published 10/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 9.88 GB | Duration: 8h 18m
Build real-world LLM apps and Retrieval-Augmented Generation (RAG) systems using LangChain.
What you’ll learn
Build production-ready LLM applications using LangChain, from basic chatbots to advanced RAG pipelines with vector databases like FAISS and Pinecone
Master document processing, text splitting, embeddings, and vector storage to create intelligent retrieval systems for generative AI applications
Implement RAG (Retrieval-Augmented Generation) architecture end-to-end, including document loading, retrieval, and generation steps with LangChain
Create custom LangChain tools and deploy a complete web summarization project using Streamlit, Groq API, and LangChain document loaders
Work with ChatPrompt templates, output parsers (JSON, Pydantic), and chain multiple components to build sophisticated LLM-powered workflows
Set up professional development environments with virtual environments, API key management, and best practices for building scalable AI applications
Understand and implement HuggingFace and Ollama embeddings for semantic search and build real-world applications with multiple vector database solutions
Deploy end-to-end AI projects with Streamlit UI, integrate Groq LLM API, and implement download features for production-ready web applications
Requirements
No prior experience with LangChain, RAG, or LLMs required – course starts from absolute basics and builds to advanced projects
A computer with internet connection and ability to install Python packages – all tools used in the course are free and open-source
Willingness to learn and experiment with generative AI technologies – course includes hands-on projects and practical implementations
No expensive API subscriptions needed – course covers free-tier options and alternatives for all services including OpenAI and Groq
Description
Master LangChain and Build AI-Powered LLM Applications from Scratch Unlock the power of LangChain — the revolutionary framework transforming how developers build Generative AI and LLM-powered applications.In this hands-on, end-to-end LangChain course, you’ll learn to design, code, and deploy real-world AI apps that use Retrieval-Augmented Generation (RAG), Embeddings, and Vector Databases like FAISS and Pinecone.By the end, you won’t just understand LangChain — you’ll be building production-grade AI systems that connect Large Language Models (LLMs) directly to your data.What You’ll Learn1. Understand what LangChain is and why it’s essential for building LLM-powered systems2. Explore LangChain components, packages, and supporting libraries3. Manage API keys and environment variables securely4. Build your first LangChain-powered LLM app step-by-step5. Master Prompt Templates, Chaining Mechanisms, and Output Parsers (String, JSON, Pydantic)6. Work with Document Loaders to ingest PDFs, text files, and web pages7. Learn advanced Text Splitting techniques for context optimization8. Create and use Embeddings with Hugging Face and Ollama9. Integrate and optimize Vector Databases for fast information retrieval10. Implement FAISS and Pinecone vector stores in real projects11. Build and deploy a complete RAG pipeline using LangChainWhy This Course?This isn’t just another AI theory class — it’s 100% project-driven.You’ll code alongside your instructor, Pratham Chandratre, and build multiple real-world AI apps that connect LLMs to real data sources. Each module moves you from concept → implementation → deployment, with clear explanations, practical examples, and hands-on exercises.By the end, you’ll have a complete portfolio of working AI projects that you can showcase to employers or clients.Who This Course Is ForAI & Data Science Students who want to step into the world of Generative AIDevelopers & Engineers looking to integrate LLMs into their applicationsResearchers & Innovators exploring RAG pipelines and intelligent retrieval systemsTech Professionals building next-gen products with LangChain, OpenAI, and PythonTechnologies You’ll MasterLangChain FrameworkOpenAI GPT APIsHugging Face TransformersOllamaFAISSPineconeVector DatabasesRAG (Retrieval-Augmented Generation)PythonYour Journey Starts HereGenerative AI is the future — and LangChain is at its core.By joining this course, you’re not just learning a tool; you’re learning how to build AI systems like ChatGPT, Copilot, and Claude — from scratch.So don’t wait — enroll today and start building your own intelligent, data-aware applications with LangChain!
Python developers who want to build generative AI applications and learn LangChain framework for creating LLM-powered solutions and RAG pipelines,Data scientists and ML engineers looking to expand into LLM development, RAG architectures, and production-ready generative AI application building,Software engineers transitioning to AI/ML roles who need hands-on experience with LangChain, vector databases, embeddings, and RAG implementations,AI enthusiasts and students eager to master modern LLM frameworks, build real-world projects, and understand how ChatGPT-like applications work,Backend developers wanting to integrate LLM capabilities into applications using LangChain tools, custom chains, and retrieval-augmented generation,Professionals seeking to upskill in generative AI technologies, document processing, semantic search, and building intelligent chatbots with RAG,Freelancers and consultants who want to offer LLM application development services and deploy AI-powered solutions for clients using LangChain,Tech entrepreneurs and startup founders looking to build AI products, understand RAG architecture, and implement vector database solutions,Computer science students preparing for AI/ML careers who want practical, project-based experience beyond theoretical knowledge of LLMs,Anyone with basic Python skills wanting to break into the booming generative AI field and build portfolio projects with cutting-edge technologies
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