---
title: "Gen AI - LLM RAG Two in One - LangChain + LlamaIndex"
url: https://manasdasgupta.com/courses/llm-rag-langchain-llamaindex/
description: "Learn to build retrieval-augmented generation (RAG) applications with both LangChain and LlamaIndex."
author: Manas Dasgupta
last_verified: 2026-09-25
---

# Gen AI - LLM RAG Two in One - LangChain + LlamaIndex

> **Bottom line:** Learn to build retrieval-augmented generation (RAG) applications with both LangChain and LlamaIndex. You work with OpenAI GPT and Google Gemini, store embeddings in ChromaDB and Pinecone, build agents, tools, query pipelines and conversational chatbots across 11 hands-on projects, and deploy a LangChain application behind a FastAPI endpoint.

- **Platform:** Udemy
- **For:** Software developers, data scientists, machine learning engineers, DevOps and QA professionals who want to build RAG applications.
- **Duration:** 9h 29m
- **Language:** English
- **Rating on Udemy:** 4.2 out of 5 (20+ ratings)
- **Learners enrolled:** 120
- **Enrol:** https://manasdasgupta.com/go/llm-rag-langchain-llamaindex/

## What you will be able to do
- Develop custom RAG applications with LangChain or LlamaIndex
- Use vector databases (ChromaDB, Pinecone) inside RAG pipelines
- Design prompts tailored to RAG systems
- Build agents and custom tools for RAG workflows
- Build conversational chatbots with memory
- Trace applications with LangGraph and deploy them with FastAPI

## Curriculum
### Introduction
LLM and prompt engineering foundations.
- Introduction to Large Language Models (LLMs)
- Introduction to Prompt Engineering
- Prompts Advanced

### Starting with LangChain
Environment setup and a first program with Google Gemini.
- Introduction to LangChain
- LangChain Environment Setup
- Using Google Gemini LLM
- Our First LangChain Program

### Learn LangChain through Projects
Five RAG projects on structured and unstructured data.
- Working with SQL Data - RAG Application
- Create a CV Upload and Search Application
- Create an Invoice Extract RAG Application
- Create a Conversational Chatbot for HR Policy Queries
- Analysis of Structured Data using Natural Language

### Getting Started with LlamaIndex
Setup and a first LlamaIndex program.
- Introduction to LlamaIndex
- LlamaIndex setup
- Our First LlamaIndex Program

### Learn LlamaIndex through Projects
Vector databases, query pipelines, agents and tools.
- RAG App using Chroma DB Vector Database
- LlamaIndex RAG with SQL Database
- LlamaIndex Query Pipelines
- LlamaIndex Complex DAG Pipeline
- Working with Agents and Tools
- Create a Calculator RAG App using ReAct Agents
- Create a Document Agent with Dynamically built Tools
- Create a Code Checker RAG App

### Creating a FastAPI for production deployment
Serve a LangChain application as an API.
- Creating a FastAPI for production deployment of your LangChain Application

## Prerequisites
- Python programming knowledge

## Frequently asked questions
### Which frameworks and models does the course use?
LangChain and LlamaIndex, with OpenAI GPT and Google Gemini models and the ChromaDB and Pinecone vector databases.

### What do I need before starting?
Working knowledge of Python programming.