Generative AI and Prompt Engineering
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to Generative AI
What is generative AI?
Evolution of language models (GPT, BERT, Claude, etc.)
Key differences: generative vs. discriminative models
Module 2: Foundations of Prompt Engineering
What is prompt engineering and why it matters
Anatomy of a good prompt
Prompt design principles (clarity, context, constraints)
Module 3: Prompt Techniques & Patterns
Zero-shot, one-shot, and few-shot prompting
Prompt chaining and re-prompting
Using system prompts and instruction formatting
Module 4: Working with Language Models (LLMs)
Overview of models like ChatGPT, Claude, and LLaMA
Temperature, top-p, and other tuning parameters
Token limitations and model context windows
Module 5: Prompt Engineering for Text Tasks
Text summarization and rewriting
Text classification and sentiment analysis
Content generation (emails, blogs, stories)
Module 6: Prompt Engineering for Structured Tasks
Code generation and debugging
Data extraction and transformation
Converting unstructured inputs into structured outputs
Module 7: Creativity with Generative AI
Art, poetry, and story generation
Role-playing and interactive use cases
Game design, quizzes, and simulations
Module 8: Evaluation & Iteration Strategies
Measuring prompt quality (relevance, coherence, creativity)
A/B testing prompts
Prompt libraries and reusable templates
Module 9: Ethics, Bias & Responsible Use
Detecting and mitigating harmful outputs
Bias in generative models
Human-in-the-loop validation for safety
Module 10: Final Capstone Project
Choose from:
Designing an AI-powered customer assistant
Building a prompt-driven educational tutor
Automating content creation with prompt pipelines
Submit prompt logic, use-case demo, and performance review
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