| Foundations of Generative AI and GPT Models | - Identify the difference between traditional machine learning, deep learning, and generative AI.
- Recognize how tokenization and embeddings are used to process language.
- Explain the concept of transformer architecture at a high level (attention, context, sequence).
- Differentiate between GPT model versions (e.g., GPT-3.5, GPT-4, GPT-4o) and their general capabilities.
| 10% | 6 |
| Prompt Engineering and Output Control | - Write clear, role-based prompts that define user, system, and assistant context.
- Implement zero -shot, few -shot, and chain -of - thought prompting strategies.
- Use delimiters, formatting, and constraints to control tone and output style.
- Debug poor responses by identifying ambiguous or missing prompt instructions.
- Apply JSON formatting or Markdown syntax in prompt design for structured output.
- Utilize temperature, top-p, and max token parameters to adjust creativity or verbosity.
| 20% | 12 |
| OpenAI API Syntax and GPT Integration | - Set up an OpenAI account and generate an API key.
- Implement basic GPT API calls using Python (OpenAI or requests libraries).
- Implement GPT API calls using JavaScript/Node.js (axios or fetch).
- Use the /v1/chat/completions endpoint to create structured conversations.
- Pass system and user roles correctly within API requests.
- Parse and render JSON output from GPT API responses.
- Handle and display GPT responses dynamically in a React (Vite) web interface.
- Use environment variables to securely store and access API keys.
- Implement API error handling (rate limits, invalid keys, token overflows).
- Adjust temperature and max tokens in code to control creativity and output length.
- Integrate GPT within N8N or Flowise nodes to automate tasks.
- Connect GPT to external APIs (e.g., Google Search, email, or Notion integration).
| 40% | 24 |
| Responsible AI, Security, and Compliance | - Identify ethical concerns in AI-generated content (bias, misinformation, copyright).
- Protect API keys and user data through environment variables or secret managers.
- Understand and apply Responsible AI principles (fairness, accountability, transparency).
- Describe privacy concerns when using user-generated prompts or proprietary data.
- Apply content filtering or moderation APIs for safe GPT responses.
- Recognize compliance standards like GDPR, FERPA, or COPPA in context.
| 15% | 9 |
| Applied GPT Use Cases and Industry Solutions | - Build a GPT-powered text summarizer or document assistant.
- Create an AI chatbot that handles customer queries or support tickets.
- Develop GPT-based automation workflows using N8N, Flowise, or Zapier.
- Use GPT for educational tutoring, content writing, or research assistance.
- Implement GPT for code completion or refactoring tasks.
| 15% | 9 |
