Rapid AI Apps Development with LangChain and LangGraph

Description:

Unlock the power of Generative AI in a single day with Rapid AI App Development with LangChain & LangGraph—a fast-paced, hands-on workshop designed for builders who want to go beyond the buzz and start creating real-world applications.

Whether you're looking to develop intelligent chatbots, automate research workflows, or build custom Retrieval-Augmented Generation (RAG) systems, this course will equip you with the tools and knowledge to make it happen—no machine learning background required.

You’ll start by mastering the foundations of LangChain, the most popular open-source framework for building LLM-based applications. Then, you'll level up by diving into LangGraph, a powerful framework for creating structured, stateful AI workflows that can adapt, reason, and persist.

Throughout the day, you'll work through guided labs and mini-projects that mirror real use cases: connecting to external data, chaining model outputs, building smart agents, and managing AI conversations over time. By the end of the course, you'll have a working AI application and the confidence to build more on your own.

Whether you're a developer exploring LLMs for the first time or a tech-savvy analyst building next-gen tools for your team, this course is your on-ramp to generative AI innovation.

Duration: 1 Day

Course Code: BDT486

Learning Objectives:

After this course, you will be able to:

• Understand LangChain’s core components and workflow structure
• Craft effective prompts and connect to LLMs
• Load and embed data using document loaders and vector stores
• Build simple chains and agent-powered tools
• Use LangGraph to implement multi-step, stateful workflows
• Construct a basic Reflection or RAG pipeline using LangGraph

• Python Programming experience is must, also understanding of Large Language Models (LLMs) is a plus.

• Software engineers, backend developers, full-stack developers, and business analysts eager to build AI-powered applications using LangChain and LangGraph.

Course Outline:

1. LangChain 101: Concepts & Setup
   • What is LangChain? Use cases and architecture
   • Data ingestion and transformation strategies
   • Lab: Installing and setting up the environment (Colab/PyCharm/VSCode)
2. Prompting and LLM Interaction
   • Prompt templates (zero/few-shot examples)
   • Lab: Prompt creation and calling LLM models
3. Chains & Data Integration
   • Building basic sequential chains
   • Loading documents and embeddings with FAISS or Chroma
   • Lab: Build a simple retrieval system
4. LangGraph Fundamentals
   • Why LangGraph? Graph-based AI workflows
   • Key concepts: nodes, state, flow control
   • Lab: RAG or Reflection agent with LangGraph
5. Agents, RAG & Reflection
   1. Intro to LangChain Agents and the ReAct pattern
   2. Building a simple agent with tools
   3. Quick overview of Reflection and RAG concepts
   4. Lab: Evaluating model performance and cost estimation
6. State & Deployment
   1. State persistence and saving memory (SQLite, files)
   2. Overview of LangGraph Cloud & LangGraph Studio
   3. Quick overview of Reflection and RAG concepts
   4. Lab: Run a LangGraph app with persistent state

Training material provided: Yes (Digital format)

Hands-on Lab: Students can install anyone of the IDEs: VS Code, PyCharm or can use Google Colab. Students will have a choice to use HuggingFace/OpenAI/Gemini or any of the open source LLMs.