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Free Lesson

Build RAG System with LangChain

Hosted by Nitin Monga

Sun, Oct 4, 2026

3:00 PM UTC (2 hours)

Virtual (Zoom)

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512 students

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Agentic AI Bootcamp: 50 Hours Live Code Along
Nitin Monga
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What you'll learn

How Retrieval-Augmented Generation (RAG) Works

Understand the core concepts behind RAG and why it’s essential for building accurate, real-world AI applications.

How to Build RAG Pipelines Using LangChain

Learn how to combine documents, embeddings, vector stores, and LLMs into a working RAG system.

How to Use ChromaDB as a Vector Store for RAG

Integrate ChromaDB with LangChain to store embeddings and run semantic similarity search for RAG.

Why this topic matters

RAG is the most practical way for enterprises to extend LLM capabilities using proprietary data. LangChain provides the tools to build these systems efficiently, from document ingestion to secure retrieval and generation. This skill is now essential for building intelligent, scalable AI solutions for the enterprise.

You'll learn from

Nitin Monga

AI Tech Founder

I’m the Founder of AI Agent Café, where we design & build intelligent AI agents, chatbots, & automation workflows that help startups, solopreneurs, and businesses scale smarter through AI.

LinkedIn - https://www.linkedin.com/in/nitinmonga-ai/

Advanced AI Course - https://maven.com/nitinai/agentic-ai-bootcamp

  • I love working at the intersection of Machine Learning, Artificial Intelligence, and Algorithmic Trading — a space where data meets decision-making and intelligence meets opportunity.

  • With 20+ years of experience in IT and over 9 years in Data Science and Artificial Intelligence, I’ve worked across different domains like Finance, Banking, Equities.

  • Built & deployed multi-agent AI apps, Agentic RAG solutions, and conversational Voice/Text AI Chatbots.


AI Entrepreneur

NVIDIA
Ogilvy
Meta
JPMorgan Chase & Co.
Teradata
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