4 weeks, from first line of code to a working agent.

• What is coding? Writing and running your first Python program with print()
• Variables and data types, taking user input, decisions with if, elif and else
• Loops (for and while), reusable functions, and storing data in lists and dictionaries
• Build four tiny apps: Age Calculator, Grade Calculator, To-Do List and Expense Tracker
• AI fundamentals: what AI is, where it is used, and the key terms in plain language
• Generative AI fundamentals: LLMs, tokens, and making your first free Gemini API call

Week 1 mini-project: AI About-Me Generator. Collect your name, interests and skills with plain Python input(), then make a single Gemini call that returns a polished introduction paragraph.

• Python data exploration: reading data, working with lists and dictionaries at scale
• Working with files and structured text in Python
• Prompt engineering foundations: instructions, context, roles and examples
• Prompt patterns: zero-shot, few-shot, chain-of-thought and structured output
• Advanced GenAI concepts: temperature, tokens, context windows and hallucinations
• Iterating on prompts to control tone, length and output format

Week 2 mini-project: Smart Text Summarizer. Paste in any paragraph and use a well-engineered prompt to return a clean 3-bullet summary, showing how small prompt changes change the output.

• Calling AI APIs from Python: keys, requests, responses and error handling
• Working with the Gemini API inside real programs
• RAG fundamentals: why grounding matters, with embeddings and retrieval in plain terms
• Loading your own documents and asking questions over them
• AI Agents: what makes software agentic, with tools, memory and reasoning loops
• Introduction to multi-agent systems and where they add value

Week 3 mini-project: Ask-My-Notes Bot. Load a short notes.txt file, ask questions, and get answers grounded in that file. A first, beginner-friendly taste of RAG and API-driven agents.

• Pick your AI Agent project, one from the capstone project ideas
• Problem statement, design, implementation, testing and demo
• Build in your own repository folder and submit via a Pull Request from your fork
• Write project documentation: problem, architecture, workflow, tech and future work
• Record a 3 to 5 minute demo video and prepare a short final presentation
• Demo Day: present your AI Agent live to mentors and peers with a demo and Q&A

Capstone: Ship your own AI Agent. Build one real AI Agent individually, submit a GitHub repository with code, README and screenshots, a 3 to 5 minute demo video, and a final presentation, then present it live on Demo Day.