AI for Working Professionals

Training Format

Live sessions
Hands-on activities
Weekly AI challenges
Mini projects
Final capstone project

Course Curriculum

Module 1: Introduction to AI & Modern Workplace Productivity

Sessions 1–2

What is AI; ML vs Deep Learning vs Generative AI; understanding LLMs; AI capabilities, limitations, myths vs reality; responsible AI usage; choosing the right AI tool; the AI application lifecycle. Hands-on with ChatGPT and Google AI Studio.

Mini Project: Build Your Personal AI Productivity Assistant

Module 2: Prompt Engineering & AI Communication

Sessions 3–4

Anatomy of an effective prompt; context, role, task, output; zero-shot, one-shot, few-shot; chain-of-thought; persona prompting; prompt templates; structured outputs (tables, JSON, reports); prompt optimization.

Mini Project: Create Your Professional Prompt Library

Module 3: AI for Everyday Work & Productivity

Sessions 5–6

AI-powered workplace productivity; research with AI; document summarization; presentation generation; AI for Excel and data analysis; AI for communication and collaboration; best practices. Tools: ChatGPT, Google AI Studio.

Mini Project: AI Office Productivity Toolkit

Module 4: Python, APIs & GitHub Essentials

Sessions 7–9

Python fundamentals (variables, conditionals, loops, functions, files); API fundamentals (REST, HTTP, JSON, authentication, API keys, calling LLM APIs); Git & GitHub (repositories, commits, branches, README, documentation, open-source best practices).

Mini Project: Build and Document an AI Python Application

Module 5: Building AI Applications

Sessions 10–12

Components of AI applications; input/output handling; prompt engineering within applications; structured responses; error handling; introduction to AI agents; connecting AI with external services. No-code platform: n8n. Build a chatbot, email assistant, FAQ assistant, meeting notes generator and content generator.

Mini Project: Company AI Assistant

Module 6: Retrieval-Augmented Generation (RAG)

Sessions 13–15

Why LLMs hallucinate; introduction to RAG; RAG architecture; embeddings; chunking strategies; vector databases (conceptual); similarity search; context retrieval; source grounding and citations; improving response accuracy.

Mini Project: Document-Based AI Knowledge Assistant

Module 7: AI Evaluation, Testing & Responsible AI

Sessions 16–18

Why AI applications need evaluation; functional testing; prompt testing; hallucination detection; response quality metrics; human evaluation; AI benchmarking; responsible AI; privacy and security; ethical AI usage; AI governance basics.

Mini Project: AI Quality Assurance Framework

Module 8: Deployment, Monitoring & Cost Awareness

Sessions 19–21

Preparing AI applications for production; FastAPI basics; Streamlit basics; deploying AI applications; hosting options; monitoring AI systems; logging and debugging; API usage monitoring; token management; cost estimation and optimization; scaling.

Mini Project: Deploy Your AI Assistant

Module 9: Capstone Project & Future of AI

Sessions 22–24

Future trends: AI agents, multimodal AI, voice AI, AI in software development, AI in business automation, emerging tools, and career pathways in AI. Participants design, build, test, deploy, and document a complete AI solution for a real-world business problem.

Capstone: Design → Build → Test → Deploy → Document → Present

Capstone Requirements

Define a real business use case
Design the solution architecture
Build the application using Python
Integrate an LLM API
Use n8n where appropriate
Implement RAG for document-based use cases
Evaluate AI responses
Deploy the application
Publish the project on GitHub
Write professional documentation
Present the solution

Weekly AI Challenges

Week 1: Create an AI assistant that helps with your daily work.
Week 2: Design reusable prompts for your profession.
Week 3: Use AI to automate a repetitive workplace task.
Week 4: Build a Python application that integrates an AI API and publish it on GitHub.
Week 5: Create an AI assistant using n8n.
Week 6: Develop a document-based chatbot using RAG.
Week 7: Evaluate and improve an AI application's performance and reliability.
Week 8: Deploy your AI application and optimize its operating costs.
Final Week: Present your capstone project with a live demo, GitHub repo, documentation, deployment link, evaluation results, and lessons learned.