Prompt Engineering for Developers

I spent several years building internal tools at Stripe before getting into developer education. One thing I learned: the same engineer can get wildly different results from AI depending on how they ask. It's not magic—it's just specificity.

Let's break this down. AI coding assistants are essentially pattern matchers trained on massive codebases. Vague requests get vague code. Specific prompts get production-ready solutions.

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Code Generation

Function Implementation

Here's what most developers write:

Weak: "Write a function that validates email"

That prompt will get you something. But probably not what you actually need.

Strong:

Write a TypeScript function that validates email addresses.

Requirements:
- Accept a string input
- Return { isValid: boolean, error?: string }
- Check for: @ symbol, valid domain, no spaces
- Handle edge cases: empty string, null/undefined
- Include JSDoc comments

Context: This is for a user registration form in a Next.js app.

The key insight here is the last line. Knowing it's for a registration form tells the AI about error messaging needs and UX considerations. Context changes output.

API Endpoint

Create a REST API endpoint for [RESOURCE] in [FRAMEWORK].

Specifications:
- Method: [GET/POST/PUT/DELETE]
- Route: [ROUTE]
- Authentication: [REQUIRED/OPTIONAL]
- Request body: [DESCRIBE]

Include input validation, error handling, and TypeScript types.

In practice, the more specific you are about your stack, the better. "Express with Zod validation" beats "Node.js backend" every time.

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Debugging

Debugging prompts work best when you show your work.

I'm getting this error: [ERROR MESSAGE]

Code:
[YOUR CODE]

Expected behavior: [WHAT SHOULD HAPPEN]
Actual behavior: [WHAT HAPPENS]

Debug step by step:
1. What's the error telling us?
2. What are potential causes?
3. How can we verify each?
4. What's the fix?

The step-by-step request is crucial. Without it, you often get "try this fix" without explanation. With it, you get reasoning you can learn from.

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Code Review

Review this code for security vulnerabilities:

[YOUR CODE]

Check for:
- Injection vulnerabilities
- Authentication issues
- Data exposure risks
- Input validation gaps

For each issue: severity, vulnerability, how exploited, recommended fix.

In practice, I use this before merging any code that handles user input or touches authentication. It catches things I miss.

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Testing

Write unit tests for this function:

[YOUR CODE]

Testing framework: [JEST/PYTEST/etc.]

Include tests for:
- Happy path
- Edge cases
- Error handling
- Boundary conditions

Specifying the testing framework matters. Jest tests look different from Pytest tests. Don't make the AI guess.

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What Actually Makes Developer Prompts Work

Be language-specific. "TypeScript function" tells the AI about types and syntax. "Function" could be anything.

Include versions. "React 18 with hooks" is different from "React 15 with class components." LLMs were trained on code from all eras.

Show your code style. If you want a particular pattern, include an example. "Write it in a similar style to: [example]" works surprisingly well.

Ask for reasoning. "Explain why this approach" turns code generation into education. You'll write better prompts next time.

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Further Reading

• Chain of Thought Prompting Guide - Essential for debugging complex code
• Claude vs ChatGPT: Which AI to Use? - Compare coding capabilities
• Common AI Prompting Mistakes - Errors that hurt code quality

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