Prompt Engineering for Developers: Get Better Code from AI
AI assistants have become indispensable tools for developers. But the quality of code suggestions varies dramatically based on how you ask. Here's how to engineer prompts that produce production-ready code.
The Problem with Basic Code Prompts
❌ Basic prompt:
"Write a function to sort users"
What you get: A generic sorting function that may not fit your needs
✓ Better prompt:
"Write a TypeScript function that sorts an array of User objects by their lastLogin date (newest first). Users have this interface: { id: string, name: string, lastLogin: Date, isActive: boolean }. Include null handling for users who have never logged in (they should appear last). Add JSDoc comments."
What you get: Exactly what you need, properly typed, documented, and edge-case handled.
Essential Elements for Code Prompts
1. Specify the Language and Framework
Always include:
- • Programming language and version
- • Framework (React 18, Node 20, Python 3.11)
- • Relevant libraries (Prisma, Axios, etc.)
2. Provide Type Definitions
Share your interfaces, types, or schemas. The AI can't guess your data structures.
3. Explain the Context
- • What system is this part of?
- • What calls this function?
- • What will use the output?
4. Specify Error Handling
"Handle these error cases:
- Network timeout (retry 3 times)
- Invalid input (throw ValidationError)
- Not found (return null, don't throw)"
5. Include Performance Requirements
"This function will be called 1000+ times per second. Optimize for performance over readability."
Prompt Templates for Common Tasks
Code Generation Template
Language: [TypeScript/Python/etc.]
Framework: [React/Express/Django/etc.]
Task: [What the code should do]
Input: [Parameters and their types]
Output: [Return type and format]
Constraints: [Performance, compatibility, etc.]
Error handling: [How to handle failures]
Code Review Template
Review this [language] code for:
1. Security vulnerabilities
2. Performance issues
3. Best practice violations
4. Potential bugs
For each issue found, explain:
- What the problem is
- Why it matters
- How to fix it (with code example)
Debugging Template
I'm getting this error: [error message]
Environment:
- [Language/version]
- [Framework/version]
- [OS if relevant]
Code causing the error:
[paste code]
What I expected: [expected behavior]
What happened: [actual behavior]
What I've tried: [debugging steps taken]
Common Mistakes Developers Make
1. Not sharing type definitions — The AI guesses wrong types
2. Forgetting the framework — Getting vanilla JS when you need React
3. No error handling specs — Code that crashes on edge cases
4. Ignoring context — Functions that don't fit your architecture
5. Not requesting tests — Untested code suggestions
Using The Prompt Fixer for Code
When you enter a code-related prompt, The Prompt Fixer:
Detects it's a technical request
Adds structure for code prompts
Includes prompts for error handling
Requests appropriate documentation
Optimizes for your target AI (Claude is great for code review, ChatGPT for generation)
Conclusion
The difference between mediocre AI code assistance and genuinely helpful suggestions comes down to prompt quality. By providing context, types, constraints, and clear requirements, you'll get code that actually works in your codebase.
Get Better Code from AI
The Prompt Fixer automatically structures your code prompts for better results. Stop getting generic snippets, start getting production-ready code.
Try The Prompt Fixer Free