Generative AI For Beginners with ChatGPT and OpenAI API
Generative Artificial Intelligence (Gen-AI)
COURSE OVERVIEW
This four-day intensive program provides a comprehensive entry point into the world of OpenAI’s generative models. It is designed to take participants from basic conversational AI usage to developing customized, API-driven applications. By the end of this course, you will understand the underlying logic of Large Language Models (LLMs), master the nuances of directing AI behavior through advanced prompting, and possess the technical foundation to integrate OpenAI’s intelligence into your own software projects.
COURSE OBJECTIVES
By the end of this course, participants will be able to:
- Deconstruct LLM Mechanics: Explain how ChatGPT processes information and generates human-like responses.
- Master the Prompting Lifecycle: Move from basic questions to structured, multi-step prompts that yield professional-grade outputs.
- Architect API Solutions: Authenticate, configure, and execute programmatic calls to OpenAI's GPT models using Python.
- Optimize AI Workflows: Integrate AI tools into daily professional tasks to automate content creation and code snippets.
- Implement Secure AI: Apply ethical guidelines and data privacy best practices to ensure safe model deployment.
- Build a Prototype: Develop a functional AI-powered tool as a final capstone project.
Duration: 4 Days / 32 Hours
Delivery Method: Classroom-based, Virtual Instructor Led Training
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.
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