Generative AI: LangChain Learning Hub

A structured curriculum for mastering Generative AI with LangChain - from basics to enterprise RAG systems

Structured, practical, production-minded

Your path to production-grade LangChain

A structured curriculum for mastering Generative AI with LangChain - from basics to enterprise RAG systems

  • 5 Lessons
  • Intermediate
  • 500+ learners
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Roadmap

Learning Path

  1. 1

    Foundations

    Core concepts of Generative AI and the LangChain mental model

  2. 2

    Components

    Prompting, models, runnables, and composition patterns

  3. 3

    RAG

    Loaders, splitters, embeddings, vector stores, retrievers

  4. 4

    Agents

    Tool calling, orchestration, and structured reasoning

  5. 5

    Capstone

    Production-aligned RAG assistant with evaluation

Syllabus

Core Lessons

01

Intro to Generative AI

Terminology, capabilities, limits, and success criteria

 02

Six Core Components

The LangChain architecture and how pieces fit

 03

Components & Setup

Runnables, pipelines, and project scaffolding

 04

Models & Prompting

Prompt templates, chat models, and structured IO

 05

Model Deep Dive

Tokenization, generation settings, and evaluation

 ★

Capstone Project

Production RAG assistant with tools and tests

Overview

Generative AI: LangChain Learning Hub is a structured curriculum that helps you progress from foundations to production-grade AI applications using LangChain and modern LLM tooling.

Format

Self-paced lessons

Hands-on first

Focus

Foundation → RAG → Agents

Sequenced path

Level

Intermediate

Python friendly

Outcome

Production-minded skills

Deployable patterns

Course Details

Instructor
Sanjan B M
Category
AI & ML / LangChain
Curriculum
5 core lessons + Capstone
Projects
Chatbot, Summarizer, Knowledge Base, Multi-tool Assistant
External Site
sanjanb.github.io/langchain

Learning Metrics

Lessons5Fundamentals to deep dive
TrackRAG + AgentsEnd-to-end builds
ModeSelf-pacedProject-led
SkillProduction-readyEvaluation & reuse

Learning Philosophy

  • Clarity over novelty: Fewer concepts, deeply understood
  • Execution model awareness: Token flow and chain composition
  • Precise naming: Prompts vs templates, retrievers vs vector stores
  • Progressive generalization: Start concrete, then abstract
  • Reusability & evaluation: Treat each artifact as testable
  • Production mindset: Real-world deployment patterns

Curriculum

View Outline
  • Lesson 1: Introduction to Generative AI
  • Lesson 2: The Six Core Components of LangChain
  • Lesson 3: LangChain Components & Setup
  • Lesson 4: Models & Prompt Foundations
  • Lesson 5: Model Component Deep Dive
  • Capstone: Multi-tenant, production-aligned RAG system
RAG Track
Loaders, splitters, embeddings, vector stores, retrievers
Agents Track
Tools & tool calling, structured reasoning, assembly

What You’ll Build

  • Chatbot: Conversational assistant with memory
  • Summarizer: Extractive and abstractive pipelines
  • Knowledge Base: Retrieval-ready document system
  • Multi-tool Assistant: Tool calling with evaluation

Who It’s For

Ideal Audience
Engineers, data scientists, builders
Prerequisites
Python, APIs; ML familiarity helpful
Time Commitment
Self-paced; project-led

Features

  • Self-paced learning with clear lesson structure
  • Hands-on projects reinforcing each concept
  • Production patterns for real-world applications
  • Clean explanations without hype
  • Progressive complexity from basics upward

Technical Focus

  • Component architecture and runnables
  • Prompt engineering patterns
  • Retrieval system design
  • Agent orchestration strategies
  • Evaluation and testing methodology

Ready to Master Generative AI with LangChain?

Join hundreds of learners building practical AI applications with structured, foundation-first learning.

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