Artificial Intelligence Productivity

Prompt Engineering Fundamentals

A practical course for designing clearer, safer, and more useful AI instructions

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Quick Course Facts
19
Self-paced, Online, Lessons
19
Videos and/or Narrated Presentations
6.2
Approximate Hours of Course Media
About the Prompt Engineering Fundamentals Course

Prompt Engineering Fundamentals is an online course that teaches you how to communicate more effectively with Artificial Intelligence tools. This practical course for designing clearer, safer, and more useful AI instructions helps students create prompts that produce stronger outputs, reduce confusion, and support real work across writing, research, analysis, coding, and decision-making.

Build Better Artificial Intelligence Prompts With Clearer Instructions

  • Learn the foundations of prompt engineering and how AI models interpret tasks, context, roles, constraints, and output requirements.
  • Practice prompt patterns for structured outputs, summaries, brainstorming, analysis, research, writing, editing, and technical support.
  • Improve reliability by testing, comparing, and refining prompts to reduce ambiguity, errors, and hallucinations.
  • Create reusable prompt templates and safer workflows that support responsible use of Artificial Intelligence in professional settings.

Prompt Engineering Fundamentals gives you a practical framework for writing effective instructions for Artificial Intelligence systems.

This course begins with the foundations of what prompt engineering is, why it matters, and how AI models respond to instructions. You will learn how to define tasks, goals, success criteria, audience, tone, boundaries, and output formats so your prompts are easier for Artificial Intelligence tools to follow.

As the course progresses, you will apply practical prompt patterns for common workflows, including structured outputs, few-shot prompting, summaries, synthesis, creative exploration, analysis, research, writing, editing, coding, and technical help. Each lesson focuses on skills you can use immediately to make AI-assisted work more accurate, organized, and useful.

You will also learn how to evaluate and improve prompts through testing, comparison, iteration, and responsible data practices. By the end of Prompt Engineering Fundamentals, you will be able to design clearer AI instructions, build reusable workflows, and approach Artificial Intelligence with more confidence, precision, and professional judgment.

Course Lessons

Full lesson breakdown

Lessons are organized by topic area and each includes descriptive copy for search visibility and student clarity.

Foundations

3 lessons

Lesson 1: What Prompt Engineering Is and Why It Matters

18 min Preview
This lesson introduces prompt engineering as the practical discipline of giving AI systems clearer instructions, better context, and more useful constraints. Students learn that prompts are not magic …

Lesson 2: How AI Models Interpret Instructions

20 min
In this lesson, students learn how AI models process instructions: not as human understanding, but as pattern-based prediction shaped by context, wording, examples, constraints, and conversation histo…

Lesson 3: Anatomy of an Effective Prompt

19 min
This lesson breaks an effective prompt into its core parts: task, context, constraints, output format, examples, and success criteria. Students learn how each part reduces ambiguity and helps an AI sy…

Prompt Design Basics

4 lessons

Lesson 4: Defining Tasks, Goals, and Success Criteria

18 min
This lesson teaches learners how to turn a vague AI request into a well-defined task with a clear goal and measurable success criteria. It focuses on the practical planning step that happens before wr…

Lesson 5: Using Context Without Overloading the Model

20 min
This lesson teaches how to give an AI model enough context to do useful work without burying it in irrelevant detail. Students learn how to separate task instructions from background information, choo…

Lesson 6: Roles, Audience, Tone, and Perspective

17 min
This lesson explains how role, audience, tone, and perspective shape the behavior of an AI response. Learners practice turning vague instructions into prompts that specify who the model should act as,…

Lesson 7: Constraints, Boundaries, and Output Requirements

21 min
This lesson teaches learners how to make prompts more reliable by setting clear constraints, boundaries, and output requirements. Students learn the difference between helpful limits and over-constrai…

Practical Prompt Patterns

5 lessons

Lesson 8: Requesting Structured Outputs

18 min
Structured outputs turn an AI response from loose prose into data you can scan, compare, validate, or pass into another workflow. In this lesson, students learn how to ask for tables, lists, JSON-like…

Lesson 9: Few-Shot Prompting with Examples

22 min
Few-shot prompting uses carefully chosen examples to show an AI model the pattern you want it to follow. Instead of only describing the task, you demonstrate the desired input-output behavior, tone, f…

Lesson 10: Prompting for Summaries and Synthesis

19 min
This lesson teaches practical prompt patterns for turning source material into useful summaries and syntheses. Students learn how to specify audience, purpose, scope, length, format, and evidence requ…

Lesson 11: Prompting for Brainstorming and Creative Exploration

18 min
In this lesson, Professor Nathan Ward shows how to use prompts for brainstorming and creative exploration without letting the model drift into vague or unusable ideas. The focus is on practical patter…

Lesson 12: Prompting for Analysis and Decision Support

21 min
This lesson teaches practical prompt patterns for using AI as an analysis and decision-support partner. Learners practice turning vague requests like What should we do? into structured prompts that de…

Applied Workflows

3 lessons

Lesson 13: Prompting for Research and Source-Aware Workflows

20 min
This lesson shows how to design prompts for research tasks where accuracy, traceability, and source quality matter. Students learn how to ask an AI system to separate known facts from uncertainty, wor…

Lesson 14: Prompting for Writing, Editing, and Rewriting

19 min
This lesson teaches practical prompting patterns for using AI as a writing, editing, and rewriting partner. Learners will practice separating the work into stages: define the audience and goal, genera…

Lesson 15: Prompting for Coding and Technical Help

22 min
In this lesson, Professor Nathan Ward shows how to prompt AI systems for coding and technical help without turning the model into an unreliable shortcut. The focus is on giving enough context, definin…

Evaluation and Improvement

2 lessons

Lesson 16: Testing, Comparing, and Iterating Prompts

21 min
This lesson teaches a practical workflow for improving prompts through testing, comparison, and iteration. Learners will move beyond judging a prompt by one impressive answer and instead evaluate prom…

Lesson 17: Reducing Errors, Hallucinations, and Ambiguity

20 min
This lesson focuses on practical ways to reduce model errors, hallucinations, and ambiguity when designing prompts. Learners will distinguish between uncertainty caused by the model, the task, missing…

Professional Practice

2 lessons

Lesson 18: Building Reusable Prompt Templates and Workflows

23 min
This lesson shows learners how to turn one-off prompts into reusable prompt templates and lightweight workflows that can be used consistently by individuals or teams. It focuses on defining template p…

Lesson 19: Responsible Prompt Engineering and Data Safety

18 min
This lesson explains how responsible prompt engineering protects people, organizations, and AI system quality. Learners will practice identifying sensitive data, reducing unnecessary exposure, setting…
About Your Instructor
Professor Nathan Ward

Professor Nathan Ward

Professor Nathan Ward guides this AI-built Virversity course with a clear, practical teaching style.