COURSE OUTLINE

Day 1: Introduction to ChatGPT and Generative AI

Focus: Building a strong conceptual foundation and getting started with the interface.

• Generative AI Fundamentals: Moving from traditional search engines to generative reasoning; understanding tokens and "probabilistic" text.
• Understanding ChatGPT: Navigating the 2026 model lineup (GPT-4o, GPT-5 series) and the specialized "Reasoning" models.
• Prompt Engineering Basics: The "Instruction-Context-Input" framework for better clarity.
• AI Use Cases: Surveying real-world applications in marketing, analysis, and software development.
• Hands-on Exercises: Interactive exploration of ChatGPT’s capabilities and identifying "hallucinations."

Day 2: Advanced Prompting Techniques

Focus: Precision control over AI outputs and professional automation.

• Prompt Optimization: Iterative refinement and the "negative prompt" (defining what the AI should avoid).
• Role-Based Prompting: Assigning personas (e.g., "Act as a Senior DevOps Engineer") to shift the model’s knowledge base.
• Few-Shot Prompting: Providing examples within the prompt to guide the AI’s style and format.
• Content and Code Generation: Using AI for boilerplate creation, debugging, and high-volume copywriting.
• AI Productivity Workflows: Connecting ChatGPT to browsers and files for live data analysis.

Day 3: OpenAI API Integration

Focus: From a chat interface to a developer-controlled platform.

• API Fundamentals: Understanding the OpenAI Playground, pricing structures, and rate limits.
• Authentication and API Calls: Managing API keys securely and writing your first request in Python.
• Building AI Applications: Exploring the Chat Completions endpoint and parameter tuning (Temperature, Top-P, Max Tokens).
• Chatbot Development: Maintaining conversation history and state management in a custom interface.
• Hands-on Integration Labs: Developing a CLI (Command Line Interface) tool that talks to the OpenAI API.

Day 4: Responsible AI and Final Project

Focus: Safety, ethics, and the demonstration of mastered skills.

• AI Ethics and Security: Addressing algorithmic bias, deepfakes, and the "Black Box" problem.
• Privacy Considerations: Best practices for handling proprietary data and PII (Personally Identifiable Information).
• Best Practices for Deployment: Moving from prototype to production; monitoring costs and user safety.
• Mini Project Implementation: Guided development time to build a specialized AI agent or tool.
• Presentation and Assessments: Showcase of the final project and peer evaluation.