Beyond 'Write me a function': Mastering Prompt Engineering for Software Development

If you're like most developers, you've probably used ChatGPT or Claude to help write code, debug issues, or generate documentation. Maybe you've had some amazing results. Maybe you've also had some frustratingly useless ones.

The difference? How you prompt.

After completing Dr. Jules White's Prompt Engineering for ChatGPT course on Coursera, I realized most developers are leaving massive productivity gains on the table. This isn't about replacing developers—it's about becoming a better one by knowing how to communicate effectively with AI tools.

Let me share what actually works.

The Core Problem: We're Treating AI Like Google

Most developers approach AI the same way they approach Stack Overflow: ask a quick question, hope for a good answer, maybe copy-paste some code.

That's the wrong mental model.

AI tools are more like pair programming with a junior dev who has read the entire internet but needs clear direction. The better your instructions, the better the output.

The Five Patterns That Changed Everything

Dr. White's course breaks down prompt engineering into actionable patterns. Here are the ones that make the biggest impact on development work:

1. Persona Pattern: Give AI a Role

Instead of generic prompts, tell the AI who it should be.

Before:

Write unit tests for this React component

After:

You are a senior front-end engineer specializing in React Testing Library
and Jest. Write comprehensive unit tests for this component that cover:
- All user interactions
- Edge cases
- Accessibility concerns
- Performance implications

[paste component code]

The difference? The AI now knows the context, expected depth, and quality bar. This pattern works for everything from code reviews to architecture discussions.

2. Template Pattern: Create Reusable Structures

This one's gold for documentation and repetitive tasks.

Example for API Documentation:

Create API documentation using this template:

## Endpoint: [name]
**Method:** [HTTP method]
**Path:** [route]

### Purpose
[What this endpoint does]

### Request Parameters
[Table of parameters with type, required/optional, description]

### Response Format
[JSON structure with explanations]

### Example Request
[Code example]

### Example Response
[Code example]

### Error Codes
[Possible errors and meanings]

Now document this endpoint: [paste code]

Build templates for:

Pull request descriptions
Technical design docs
Bug reports
Code review feedback
Migration plans

Save them in your notes. Reuse them constantly.

3. Recipe Pattern: Multi-Step Instructions

When you need AI to follow a specific process, lay out the steps explicitly.

For refactoring legacy code:

Follow these steps to refactor this component to use modern best practices:

1. Identify any deprecated patterns or anti-patterns in the current code
2. List opportunities to improve performance and readability
3. Suggest modern alternatives (hooks, composition, etc.)
4. Identify missing error handling or edge cases
5. Recommend updated TypeScript types if applicable
6. Provide the refactored code with explanations for each change

Component code:
[paste code]

This pattern works incredibly well for debugging, migration planning, and optimization tasks.

4. Reflection Pattern: Make AI Double-Check Itself

Add this to the end of complex prompts:

After providing your solution, reflect on:
- What assumptions did you make?
- What edge cases might break this?
- What would a senior engineer critique about this approach?
- Are there performance implications I should know about?

This single addition catches subtle bugs and architectural issues before they hit your codebase.

5. Few-Shot Learning: Show Examples

For style-specific needs, show the AI what you want.

For code style consistency:

I need you to write TypeScript following this exact style:

Example 1:
// Show your preferred pattern

Example 2:
// Show another example

Now write a [description] following the same patterns.

This is especially useful for maintaining consistency across a codebase or matching team conventions.

Real-World Applications in Daily Development

Here's how these patterns apply to common engineering tasks:

Sprint Planning Prep

You are a technical lead reviewing a feature request. Analyze this requirement
and provide:

1. Technical complexity assessment (1-5 scale with reasoning)
2. Potential technical risks or unknowns
3. Dependencies on other systems or teams
4. Suggested breakdown into smaller tasks
5. Questions to ask the product owner

Requirement: [paste story]

Code Review Assistance

Review this pull request as a senior front-end engineer focusing on:
- Performance implications for high-traffic applications
- Accessibility issues
- Potential race conditions or edge cases
- Framework best practices
- Opportunities for reusability

[paste diff]

Documentation Generation

Using the template I provided earlier, generate comprehensive documentation
for this utility function. Focus on real-world use cases a developer
would encounter in a production application.

[paste code]

Architecture Planning

You are a senior software architect. I need to build [feature description].

Analyze this requirement and provide:

1. Recommended architecture pattern (MVC, microservices, event-driven, etc.) with reasoning
2. Technology stack suggestions with pros/cons for this specific use case
3. Data flow and component structure diagram (in text/ASCII)
4. Potential scalability concerns and how to address them
5. Security considerations I should plan for
6. Testing strategy recommendations
7. Deployment and infrastructure needs

Feature requirements:
[paste requirements]

Constraints:
- Current tech stack: [list]
- Expected scale: [users/traffic]
- Team size: [number]

Technical Documentation

You are a technical writer specializing in developer documentation.
Create comprehensive documentation for [system/feature/process].

The documentation should include:

1. Overview - What it is and why it exists
2. Key concepts and terminology
3. Architecture/flow diagrams (describe in text)
4. Step-by-step usage guide with code examples
5. Common pitfalls and troubleshooting
6. FAQs based on likely developer questions

Target audience: [junior devs/senior devs/mixed team]
Tone: [conversational/formal/technical]

Context:
[paste relevant code, system description, or process details]

Build Your Prompt Library

As you work with these patterns, you'll naturally develop your own collection of go-to prompts. Here are the categories worth maintaining:

Sprint & Planning

Feature complexity analysis
Technical risk assessment
Story breakdown and estimation
Architecture decision templates

Code Quality

Code review checklists
Refactoring planners
Performance analysis prompts
Security review templates

Documentation

API documentation generators
Technical design doc structures
Team process documentation
Troubleshooting guides

Problem Solving

Debug workflow assistants
Architecture planning prompts
Migration strategy templates
Performance optimization guides

Save these in whatever system works for you—Notion, Obsidian, a text file, or even a GitHub repo. The key is making them easily accessible so you actually use them.

Advanced Technique: Chain of Thought Prompting

For complex problems, explicitly ask the AI to show its reasoning:

Explain your thought process step-by-step as you solve this problem:

Problem: [describe complex architectural decision]

Walk me through:
1. How you're breaking down the problem
2. What trade-offs you're considering
3. Why you're choosing one approach over another
4. What you'd do differently at different scales

This is incredibly valuable for learning and for validating architectural decisions.

What Doesn't Work (Lessons Learned)

❌ Vague prompts: "Make this better" or "Optimize this code"

AI needs specific criteria for "better"

❌ Assuming context: AI doesn't know your codebase, team conventions, or business rules

Always provide relevant context explicitly

❌ One-and-done prompts: Expecting perfect output on the first try

Iterate. Refine. Provide feedback.

❌ Blind copy-paste: Not reviewing or understanding the output

AI makes mistakes. You're still the engineer.

The Mindset Shift

The biggest takeaway from Dr. White's course wasn't a specific technique—it was a mindset shift:

AI is a thought partner, not a code generator.

Use it to:

Explore architectural options
Challenge your assumptions
Generate test cases you didn't think of
Document your decisions
Learn new patterns and approaches

The best code written with AI assistance is code you fully understand and could have written yourself—AI just helped you get there faster and with fewer blind spots.

Start Small, Build Habits

Don't try to revolutionize your workflow overnight. Start with:

Pick one pattern - The Persona Pattern is a great starting point
Choose one repetitive task - Documentation, code reviews, whatever drains your time
Create a template - Refine it over a week or two
Save it - Make it easily accessible (Notion, Obsidian, a text file—whatever works)
Iterate - Improve based on results

After a month, you'll wonder how you worked without it.

Resources & Next Steps

Dr. Jules White's Course: Prompt Engineering for ChatGPT on Coursera
Prompt Engineering Guide: promptingguide.ai

Final Thought

AI hasn't made developers obsolete—it's made effective developers more productive. The engineers who thrive in the next decade won't be the ones who resist AI or blindly depend on it.

They'll be the ones who learn to wield it like a precision tool.

And that starts with better prompts.

What patterns have you found effective in AI-assisted development? I'd love to hear about it—connect with me on LinkedIn.