Generative AI & LLM Developer Mastery – 12 Weeks (Hands-On)

Learn to build real AI applications using OpenAI, LangChain, RAG, LangGraph, Neo4j, AWS & more

🔹 Course Highlights

• 100% Hands-on practical learning

• Build 12+ real-world AI applications

• Learn OpenAI API, LangChain, RAG, Vector Databases, LangGraph, Neo4j

• Deploy your AI apps on AWS, Docker & LangFuse

• Perfect for Developers, Students, and Working Professionals

📅 12-Week Syllabus (With Tools & Mini Projects)

Week 1 – Foundations of LLMs & Generative AI

Focus: Understanding LLMs, API basics
Deliverables: Local dev setup, First OpenAI API call
Tools: Python, OpenAI API, GitHub
Mini Project: “Hello AI” – creative text generator

Week 2 – AI Agents & Agentic Workflows

Focus: Agent architecture, chains
Deliverables: Simple LangChain chain
Tools: LangChain, OpenAI
Mini Project: Decision-making agent for domain queries

Week 3 – Building Basic Chat Applications

Focus: Memory, chat formatting
Deliverables: Chatbot with short-term memory
Tools: LangChain, Qdrant/PGVector
Mini Project: FAQ assistant chatbot

Week 4 – Chat Over Large Documents

Focus: Document loaders, embeddings
Deliverables: Chatbot over a single PDF
Tools: Qdrant DB / Pinecone / LangChain
Mini Project: “Ask my PDF” bot for an eBook

Week 5 – Retrieval Augmented Generation (RAG)

Focus: RAG pipelines, query optimization
Deliverables: End-to-end RAG workflow
Tools: LangChain, Vector Stores
Mini Project: RAG-powered news summariser

Week 6 – Memory-Aware AI & Graph DB

Focus: Long-term memory AI
Deliverables: Graph-based memory system
Tools: Neo4j, LangChain
Mini Project: Entity extraction & graph visualisation

Week 7 – Multi-Modal LLM Applications

Focus: Vision + Text
Deliverables: Image-based Q&A
Tools: OpenAI Vision API, LangChain
Mini Project: Multi-modal chatbot

Week 8 – Security, Guardrails & MCP

Focus: Data safety, redaction
Deliverables: Guardrails pipeline
Tools: Guardrails AI, LangChain, MCP
Mini Project: Sensitive data redaction tool

Week 9 – LangGraph & Checkpointing

Focus: Multi-agent workflows
Deliverables: LangGraph workflow with retries
Tools: LangGraph, LangChain
Mini Project: Multi-agent research report generator

Week 10 – Human-in-the-Loop AI (HITL)

Focus: Tool binding, approval steps
Deliverables: AI app with human confirmation
Tools: LangChain Tools, API integrations
Mini Project: Email drafting assistant with approval

Week 11 – Model Fine-Tuning & LLM-as-Judge

Focus: Evaluations & fine-tuning
Deliverables: Fine-tuned small LLM
Tools: OpenAI Fine-Tuning, Perplexity API
Mini Project: Resume scoring AI (LLM-judge)

Week 12 – Deployment & Capstone

Focus: Production-ready AI apps
Deliverables: Fully deployed app
Tools: AWS, Docker, LangFuse
Mini Project: Capstone – Your AI project presentation

✔ Real-World Use Cases You Will Build

You will master AI by building applications such as:

🔸 Business Use Cases

• AI Customer Support Bot

• Automated Email Writer

• AI Meeting Summariser

• Lead Qualification AI

• HR Resume Screener

• Policy & SOP Chatbot

• Finance RAG Assistant

🔸 Technical Use Cases

• PDF Chatbot for documentation

• Image-based question bot

• Multi-agent research system

• Model evaluator / judge

• AI code assistant

• LLM performance dashboard (Langfuse)

🔸 Industry Use Cases

• Healthcare: Prescription reader (Vision)

• Real estate: Project details Q&A bot

• EdTech: Student learning assistant

• Legal: Contract summariser

• Banking: Complaint classification bot

🎓 Who Should Join?

• Developers (Python/JS)

• Students & freshers

• Working professionals switching to AI

• Founders building AI products

• Data science & cloud engineers

💼 What You Will Achieve

• Build full-stack AI applications

• Work with vector DBs, LangChain, LangGraph

• Understand RAG, memory, agents, multimodal AI

• Get deployment-ready with AWS & Docker

• Build a portfolio of 12+ AI apps