WGU D685 Practical Applications of
Prompt Engineering Study Guide with
Complete Solutions
Table of Contents
How to Use This Guide
Course Overview
Section 1: Generative AI and Prompt Engineering
Section 2: Crafting Effective Prompts
Section 3: Prompt Evaluation and Image Generation
Section 4: Advanced Applications and Ethics
Quick Reference: Prompt Engineering Cheat Sheet
Study Plan Recommendations
Practice Scenarios
Common Mistakes to Avoid
Assessment Preparation Tips
Additional Resources
Final Checklist Before Objective Assessment
Success Indicators
Cram Sheet
,How to Use This Guide
This study guide serves two purposes:
• Pre-Course Assessment: Review this guide before starting the course to identify areas requiring
additional study
• Course Companion: Use alongside course materials to reinforce learning and prepare for
assessments
Each section includes key concepts, practical examples, and self-assessment questions to gauge your
readiness.
Course Overview
Course Competencies:
• Explain why prompt engineering is necessary
• Create effective prompts considering scope, specificity, and context
• Evaluate prompt effectiveness and adjust for relevant results
• Improve analytical investigations through strategic prompting
Assessment Structure:
• Lesson Quizzes (within chapters)
• Pre-Assessment (practice for final)
• Objective Assessment (final, proctored)
, Section 1: Generative AI and Prompt Engineering
Core Concepts – What You Need to Know:
AI Fundamentals:
• Narrow AI vs. General AI: Narrow AI (weak AI) performs specific tasks like chess or image
recognition; General AI (strong AI) would possess human-level intelligence across all domains (still
theoretical)
• Machine Learning Types: Supervised learning (labeled data), Unsupervised learning (pattern
discovery), Reinforcement learning (reward-based)
• Neural Networks and Deep Learning: Interconnected layers of artificial neurons that process
information
• Large Language Models (LLMs): AI systems trained on massive text datasets to understand and
generate human-like text
AI Capabilities:
• Pattern recognition across various data types
• Natural language processing and understanding
• Computer vision and image recognition
• Real-time data processing and analysis
• Prediction and classification tasks
AI Limitations:
• Lacks true intelligence, consciousness, and self-awareness
• Limited by training data quality and scope
• Cannot experience genuine emotions or subjective experiences
• Struggles with truly open-ended, creative problem-solving
• May perpetuate biases present in training data
• Cannot understand context the way humans do
Prompt Basics:
• Prompt: The input instruction or question given to an AI system
• Interface: The method of interaction between humans and AI
• Specificity: The level of detail and clarity in prompts
• Key Parameters: Temperature (creativity level), context (background info), output format (how to
present results)
Prompt Engineering Study Guide with
Complete Solutions
Table of Contents
How to Use This Guide
Course Overview
Section 1: Generative AI and Prompt Engineering
Section 2: Crafting Effective Prompts
Section 3: Prompt Evaluation and Image Generation
Section 4: Advanced Applications and Ethics
Quick Reference: Prompt Engineering Cheat Sheet
Study Plan Recommendations
Practice Scenarios
Common Mistakes to Avoid
Assessment Preparation Tips
Additional Resources
Final Checklist Before Objective Assessment
Success Indicators
Cram Sheet
,How to Use This Guide
This study guide serves two purposes:
• Pre-Course Assessment: Review this guide before starting the course to identify areas requiring
additional study
• Course Companion: Use alongside course materials to reinforce learning and prepare for
assessments
Each section includes key concepts, practical examples, and self-assessment questions to gauge your
readiness.
Course Overview
Course Competencies:
• Explain why prompt engineering is necessary
• Create effective prompts considering scope, specificity, and context
• Evaluate prompt effectiveness and adjust for relevant results
• Improve analytical investigations through strategic prompting
Assessment Structure:
• Lesson Quizzes (within chapters)
• Pre-Assessment (practice for final)
• Objective Assessment (final, proctored)
, Section 1: Generative AI and Prompt Engineering
Core Concepts – What You Need to Know:
AI Fundamentals:
• Narrow AI vs. General AI: Narrow AI (weak AI) performs specific tasks like chess or image
recognition; General AI (strong AI) would possess human-level intelligence across all domains (still
theoretical)
• Machine Learning Types: Supervised learning (labeled data), Unsupervised learning (pattern
discovery), Reinforcement learning (reward-based)
• Neural Networks and Deep Learning: Interconnected layers of artificial neurons that process
information
• Large Language Models (LLMs): AI systems trained on massive text datasets to understand and
generate human-like text
AI Capabilities:
• Pattern recognition across various data types
• Natural language processing and understanding
• Computer vision and image recognition
• Real-time data processing and analysis
• Prediction and classification tasks
AI Limitations:
• Lacks true intelligence, consciousness, and self-awareness
• Limited by training data quality and scope
• Cannot experience genuine emotions or subjective experiences
• Struggles with truly open-ended, creative problem-solving
• May perpetuate biases present in training data
• Cannot understand context the way humans do
Prompt Basics:
• Prompt: The input instruction or question given to an AI system
• Interface: The method of interaction between humans and AI
• Specificity: The level of detail and clarity in prompts
• Key Parameters: Temperature (creativity level), context (background info), output format (how to
present results)
