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Generative AI: Prompt Engineering Basics

Chapters

1Foundations of Generative AI

2LLM Behavior and Capabilities

Pretraining and Fine-Tuning Instruction Following and Alignment RLHF and Preference Optimization Sensitivity to Wording and Order Length Bias and Cutoff Realities Hidden Biases and Stereotypes Refusals and Safety Behavior Non-Determinism and Sampling Variance Stop Sequences and Output Control System Message Priority Tool-Use Affordances Function Calling at a Glance Style and Tone Emulation Domain Transfer and Generalization When Models Say “I Don’t Know”

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/LLM Behavior and Capabilities

LLM Behavior and Capabilities

18062 views

Understand alignment, sensitivity to phrasing, non-determinism, and other behavioral properties that your prompts must account for.

Content

7 of 15

Refusals and Safety Behavior

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