About Course

This 2-week intensive evening program is designed for developers who want to build cutting-edge AI applications using LangChain and Retrieval-Augmented Generation (RAG) techniques. The course walks you through LangChain’s core concepts, tools, chains, memory, vector databases, and document processing. In the second week, you’ll build end-to-end RAG systems that combine local documents and LLMs to deliver accurate, contextual responses. By the end of the course, you will deploy your own LangChain-powered RAG application and gain practical skills in production-ready AI integration.

What Will You Learn?

  • Learn how to build contextual AI apps that go beyond static prompt engineering
  • Master the use of LangChain chains, tools, agents, and memory modules
  • Build document-aware apps using RAG (Retrieval-Augmented Generation)
  • Deploy AI apps with front-end (Streamlit) or as APIs (LangServe/FastAPI)
  • Stay ahead with cutting-edge techniques used in real-world AI systems

Course Content

Day 1 – Introduction to LangChain

  • What is LangChain?
    00:20:00
  • LLMs & Prompt Chains
    00:20:00

Day 2 – LangChain Basics: LLMChains, PromptTemplates

Day 3 – Tools, Agents & LangChain Memory

Day 4 – Vector Stores & Embeddings

Day 5 – Document Loaders & Text Splitters

Day 6 – Retrieval-Augmented Generation (RAG) Basics

Day 7 – Advanced RAG: Refining & Map-Reduce Chains

Day 8 – Building Multi-Document QA Apps

Day 9 – Deploying LangChain Apps

Day 10 – Capstone Project + Scaling Tips

Available in:
E
₹9,500.00

Material Includes

  • Starter code for LangChain workflows and agents
  • Sample loaders for PDFs, websites, Notion, markdown, and more
  • Pre-configured FAISS/ChromaDB vector store templates
  • Capstone project guide and presentation template
  • Access to deployment templates using Streamlit and LangServe

Requirements

  • Proficiency in Python and working with APIs
  • Familiarity with basic concepts of LLMs (OpenAI, Anthropic, etc.)
  • A working laptop with Python 3.8+ and access to terminal
  • API keys for OpenAI or other LLM providers (instructions will be given)
  • Install required libraries such as LangChain, FAISS, ChromaDB, Streamlit

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Audience

  • Python developers exploring generative AI tools
  • Engineers building document-based chatbots or assistants
  • AI/ML enthusiasts wanting to implement RAG systems
  • Product builders working with OpenAI or LLM APIs
  • Hackathon participants and startup teams developing AI features

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