Duration: 30–40 hours (flexible)
Format: 100% labs, mini-projects, and capstone
Prerequisites: Basic Python + API usage (no ML background required)

Module 1: Generative AI Setup & First Builds

Outcome: Learners can run and interact with GenAI models locally and via APIs.

Hands-on Labs

• Set up Python environment (venv, notebooks)

• Use OpenAI / compatible LLM APIs

• Build:

  • Prompt → Response CLI tool

  • Simple chat interface (terminal-based)

• Experiment:

  • Temperature, max tokens, system prompts

Mini Project

• Build a personal AI assistant with configurable personality

Module 2: Prompt Engineering by Experimentation

Outcome: Learners control model outputs reliably.

Hands-on Labs

• Prompt patterns:

  • Role prompting

  • Few-shot prompting

  • Chain-of-thought style prompts

• Prompt debugging:

  • Reduce hallucinations

  • Improve accuracy

• Build:

  • Prompt testing harness (compare outputs)

  • Prompt versioning system

Mini Project

• Prompt-powered tool (resume reviewer / lesson generator / idea validator)

Module 3: Working with Documents (RAG – Practical Only)

Outcome: Learners build AI that answers from custom data.

Hands-on Labs

• Load PDFs, text files, CSVs

• Chunking & embeddings

• Vector databases (FAISS / Chroma)

• Build:

  • Document Q&A system

  • AI chatbot over course notes or company docs

Mini Project

• AI Knowledge Bot trained on custom documents

Module 4: Structured Outputs & Tool Use

Outcome: Learners make AI produce reliable, structured results.

Hands-on Labs

• JSON & schema-based outputs

• Validation of AI responses

• Function / tool calling

• Build:

  • AI that extracts data from text

  • AI decision engine using tools

Mini Project

• AI Automation Agent (e.g., email classifier, lead scorer)

Module 5: Image Generation (Hands-On Creation)

Outcome: Learners generate, edit, and automate images.

Hands-on Labs

• Text-to-image generation

• Image variations & edits

• Prompting for styles, branding

• Build:

  • Image generator app

  • Bulk image creation pipeline

Mini Project

• Brand Image Generator for marketing or content

Module 6: Building AI Applications (Frontend + Backend)

Outcome: Learners deploy real GenAI apps.

Hands-on Labs

• Backend:

  • FastAPI / Flask with LLMs

• Frontend:

  • Streamlit / simple React

• Features:

  • Chat memory

  • User inputs

  • Logging

• Build:

  • Full-stack AI app

Mini Project

• Production-ready AI web app

Module 7: Agents & Multi-Step Reasoning

Outcome: Learners build autonomous AI workflows.

Hands-on Labs

• Single-agent workflows

• Multi-step task execution

• Tool-using agents

• Build:

  • Research agent

  • Planner + executor agent

Mini Project

• Autonomous AI Agent that completes a real task

Module 8: Capstone (Hands-On Only)

Outcome: Learners demonstrate real-world capability.

Capstone Options

• AI Tutor

• AI HR Assistant

• AI Sales Copilot

• AI Content Factory

• AI Internal Company Bot

Deliverables

• Working application

• Demo video

• Prompt & architecture documentation

Tools Used (Practically)

• Python

• OpenAI / compatible LLM APIs

• LangChain / LlamaIndex (optional)

• Vector DBs

• Streamlit / FastAPI

• Git & deployment basics

Assessment (No Exams)

• Lab completion

• Mini-projects

• Capstone demo