Generative AI: Prompt Engineering Basics

Chapters

1Foundations of Generative AI

What Is Generative AI AI vs ML vs Deep Learning Transformer Architecture Primer Tokens and Tokenization Probabilities and Next-Token Prediction Temperature and Top-p Sampling Context Window and Limits Prompt–Response Loop System, Developer, and User Messages Capabilities and Limitations Hallucinations and Uncertainty Determinism vs Stochasticity Safety Layers and Moderation Evaluation Mindset from Day One Useful Mental Models of LLMs

2LLM Behavior and Capabilities

3Core Principles of Prompt Engineering

4Writing Clear, Actionable Instructions

5Roles, Personas, and System Prompts

6Supplying Context and Grounding

7Examples: Zero-, One-, and Few-Shot

8Structuring Outputs and Formats

9Reasoning and Decomposition Techniques

10Iteration, Testing, and Prompt Debugging

11Evaluation, Metrics, and Quality Control

12Safety, Ethics, and Risk Mitigation

13Tools, Functions, and Agentic Workflows

14Retrieval-Augmented Generation (RAG)

15Multimodal and Advanced Prompt Patterns

Courses/Generative AI: Prompt Engineering Basics/Foundations of Generative AI

Foundations of Generative AI

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Establish how modern LLMs generate text, the role of tokens and probabilities, and the constraints that shape prompt behavior.

Content

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Useful Mental Models of LLMs

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