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Generative AI 2 Months Intermediate Live on Zoom

LangChain & LlamaIndex Mastery

Master the two most popular LLM frameworks. Build chains, agents, RAG pipelines and production AI applications over 2 months of intensive live training.

4.7 (16 reviews) 110+ enrolled Certificate included

LangChain and LlamaIndex are the essential frameworks for building LLM-powered applications. This 2-month course gives you deep expertise in both frameworks.

From simple chains to complex agent systems, you will build real applications in every session and understand when to use each framework for maximum effectiveness.

  • LangChain architecture & LCEL
  • Prompt templates & output parsers
  • Memory systems (buffer, summary, vector)
  • Chains & sequential processing
  • Callbacks & streaming
  • LangSmith for debugging

  • Document loaders & node parsing
  • Index types (vector, list, tree, keyword)
  • Query engines & response synthesis
  • Structured outputs & Pydantic
  • Custom retrievers
  • LlamaCloud & managed indices

  • LangChain agents & LangGraph
  • LlamaIndex agents & tools
  • RAG with both frameworks compared
  • Advanced retrieval strategies
  • Evaluation with LangSmith & LlamaIndex
  • Multi-modal applications

  • LangServe for API deployment
  • Streaming UIs with Streamlit/Gradio
  • Caching & performance optimization
  • Cost management in production
  • Monitoring & observability
  • Capstone: production LLM application

What You'll Learn

  • Build complete applications with LangChain and LlamaIndex
  • Create advanced RAG pipelines with both frameworks
  • Build and deploy AI agents using LangGraph
  • Evaluate LLM applications with LangSmith
  • Choose the right framework for each use case
  • Deploy production LLM applications

Tools & Technologies You'll Master

Python
LangChain
LangGraph
LlamaIndex
OpenAI
LangSmith
Streamlit

You should know Python. Familiarity with LLM concepts is helpful but not required.

Live Zoom classes, Monday to Thursday, 7:00 PM to 8:30 PM IST. 4 days per week, 90 minutes each.

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