Generative AI: Prompt Engineering Basics

Chapters

1Foundations of Generative AI

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

Human Evaluation Rubrics LLM-as-Judge Techniques Objective vs Subjective Metrics Accuracy, Fluency, and Coverage Safety and Harms Assessment Cost, Latency, and Quality Tradeoffs Acceptance Thresholds Inter-Rater Reliability Sampling and Test Sets Calibration and Score Normalization Prompt Scorecards Dashboards and Monitoring Continuous Evaluation Loops Drift and Degradation Detection Closing the Feedback Loop

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/Evaluation, Metrics, and Quality Control

Evaluation, Metrics, and Quality Control

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Measure output quality with human and automated methods, track performance, and close the loop with monitoring.

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Calibration and Score Normalization

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