Generative AI: LangChain

Learning Hub

A structured curriculum for mastering Generative AI with LangChain

This site helps learners master Generative AI with strong foundations and practical building approaches. From LangChain basics to enterprise RAG systems - your journey to AI expertise starts here.

Home

About

I'm Sanjan B Man engineer and writer focused on making AI concepts practical and accessible.

My areas of interest:

  • Generative AI systems and application architecture
  • Learning frameworks that build durable understanding
  • Clear abstractions over implementation complexity

I believe in learning that compounds across domains rather than chasing trends.

"The best way to understand AI is to build with it."


About This Site

This is a structured learning path for building real applications with LangChain and modern LLM tooling. The focus is on developing precise conceptual understanding and building production-ready systems step by step.

Why this course exists: Most AI tutorials teach you to copy-paste code. This course teaches you to think in AI patterns and build systems that scale.

Learning Philosophy

  • Clarity over novelty – Fewer concepts, deeply understood
  • Execution awareness – Understanding how components actually work together
  • Precise naming – Distinguishing tools vs. agents, prompts vs. templates
  • Progressive building – Start concrete, then generalize
  • Testable components – Every piece should be reusable and evaluable

What You'll Find

Curriculum Sequenced path from fundamentals → retrieval → agents
Projects Guided builds reinforcing core patterns
Capstone Enterprise RAG system integrating all concepts

Learning outcome: By the end, you'll think like an AI engineer, not just follow tutorials.

Course Structure

Part 1: Fundamentals
Components, models, prompts, parsing, chains, memory

Part 2: Retrieval (RAG)
Loaders, embeddings, vector stores, end-to-end RAG systems

Part 3: Agents
Tools, reasoning, structured workflows, autonomous systems

Projects
Chatbot → Summarizer → Knowledge Base → Assistant → Enterprise RAG


How to Use This Site

  1. Start with curriculum if you want the complete path
  2. Preview lessons by reading "Why this matters" sections first
  3. Build immediately – complete projects after prerequisite lessons
  4. Refactor later – revisit code once you learn new abstractions
  5. Keep notes – maintain a personal glossary for precision

Connect

Let's build the future of AI together.

Questions, feedback, or collaboration ideas? Feel free to reach out.


Ready to start?