Prompt Engineering (PE)

Goals

1. Describe Prompt Engineering (PE).
2. Skillfully apply PE to reliably get better results from AI systems.
3. Get high payoffs from PE (higher-quality outputs, less wasted time, more leverage from AI).

What?

Prompt Engineering (PE) is the skill of deliberately designing inputs (prompts) to an AI system so that the system produces more accurate, useful, and reliable outputs.

In practical terms: - A prompt is the information you give an AI system (instructions, context, examples, constraints). - Engineering means designing that input intentionally, not casually.

PE is not: - Memorizing magic phrases - Guessing what the model “likes” - A one-shot activity done once and forgotten

PE is: - A structured way of communicating goals, context, and constraints to an AI system - An iterative process guided by feedback - A transferable skill that applies across tasks (writing, coding, planning, analysis)

At its core, PE is about reducing ambiguity and aligning the model’s behavior with your intent.

Why?

Learning and applying PE pays off because it allows you to:

• Get higher-quality results from the same AI system
• Save time by reducing retries and frustration
• Tackle more complex or subtle tasks successfully
• Use AI as a reliable collaborator rather than a novelty tool
• Scale your thinking, writing, and problem-solving

Without PE: - Outputs are often vague, misaligned, or inconsistent - Users blame the tool instead of fixing the input - Effort increases while payoff stays low

With PE: - Small improvements in prompts can produce large improvements in results - Payoffs compound over time as prompts and patterns are reused

How

Causal Model

Clear Intent
+ Relevant Context
+ Explicit Constraints
+ Examples (when helpful)
→ Higher-Quality Output
→ Review Results
→ Refine Prompt
→ Repeat

Details

1. Clear Intent
   • State what you want the AI to do.
   • Avoid vague requests like “help” or “thoughts.”
   • Use action-oriented language (explain, generate, analyze, critique).
2. Relevant Context
   • Provide background the model needs to succeed.
   • Include audience, purpose, prior constraints, or definitions.
   • Exclude irrelevant detail that increases noise.
3. Explicit Constraints
   • Specify format, length, style, tone, or structure.
   • Constraints reduce ambiguity and guide the model’s choices.
4. Examples (When Helpful)
   • Show the model what “good” looks like.
   • Examples are especially powerful for style, structure, or edge cases.
5. Review and Iterate
   • Treat outputs as feedback, not final answers.
   • Adjust the prompt based on what worked and what didn’t.
   • Iteration is a feature, not a failure.

The key insight: Most output problems are input problems.
Improving the prompt is usually the highest-leverage fix.

Tips and Pitfalls (Optional)

• Tip: Start simple, then add structure only as needed.
• Tip: Reuse and refine successful prompts.
• Pitfall: Overloading the prompt with unnecessary detail.
• Pitfall: Treating the first output as the “answer” instead of feedback.

Relationships (Optional)

• Prompt Engineering supports:
  • Coding
  • Writing
  • Critical thinking
  • Problem solving
• PE pairs naturally with Tasks-with-Feedback (TwFs), where iteration and edge cases are practiced.

Resources

• Master Prompt Engineering for Beginners! (7 Essential Tips) – AI Foundations (YouTube)

Success Criteria

A learner has succeeded with this lesson when they can:

1. Describe
   • Clearly explain what Prompt Engineering is and is not.
2. Apply
   • Design prompts that specify intent, context, and constraints.
   • Improve AI outputs by revising prompts rather than starting over.
3. Evaluate
   • Diagnose low-quality outputs as prompt-design problems.
   • Explain why a revised prompt is likely to work better.
4. Perform
   • Consistently obtain higher-quality, more reliable AI outputs on real tasks.
5. Value
   • Recognize Prompt Engineering as a high-payoff, reusable skill rather than a collection of tricks.