Master LangChain: Build LLM Apps & RAG Pipelines with Python

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 ofLangChain— the revolutionary framework transforming how developers buildGenerative AI and LLM-powered applications.

In thishands-on, end-to-end LangChain course, you’ll learn to design, code, and deploy real-worldAI appsthat useRetrieval-Augmented Generation (RAG),Embeddings, andVector DatabaseslikeFAISSandPinecone.

By the end, you won’t just understand LangChain — you’ll bebuilding production-grade AI systemsthat connectLarge Language Models (LLMs)directly to your data.

What You’ll Learn

1. 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 completeRAG pipelineusing LangChain

Why This Course?

This isn’t just another AI theory class — it’s100% project-driven.

You’ll code alongside your instructor,Pratham Chandratre, and build multiplereal-world AI appsthat connectLLMs to real data sources. Each module moves you fromconcept → implementation → deployment, with clear explanations, practical examples, and hands-on exercises.

By the end, you’ll have a completeportfolio of working AI projectsthat you can showcase to employers or clients.

Who This Course Is For

AI & Data Science Studentswho want to step into the world of Generative AI

Developers & Engineerslooking to integrate LLMs into their applications

Researchers & Innovatorsexploring RAG pipelines and intelligent retrieval systems

Tech Professionalsbuilding next-gen products withLangChain,OpenAI, andPython

Technologies You’ll Master

LangChain Framework

OpenAI GPT APIs

Hugging Face Transformers

Ollama

FAISS

Pinecone

Vector Databases

RAG (Retrieval-Augmented Generation)

Python

Your Journey Starts Here

Generative 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 tobuild AI systems like ChatGPT, Copilot, and Claude — from scratch.

So don’t wait —enroll todayand start building your own intelligent, data-aware applications with LangChain!

Who this course is for:
- 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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