ChatGPT Prompt Templates for Developers — Copy and Use Today
Practical, copy-ready prompt templates for the tasks developers use AI for most — code review, debugging, documentation, architecture, and refactoring.
The quality of an AI response is almost entirely determined by the quality of the prompt. A well-structured prompt specifies the role, the task, the constraints, and the desired output format. A vague prompt produces a vague response.
The templates below follow a consistent pattern: they define a role, a concrete task, and clear output requirements. Use them as-is or adapt them to your stack.
Code Review
You are a senior software engineer specialising in [LANGUAGE/FRAMEWORK]. Review the following code for: 1. Bugs and logic errors 2. Security vulnerabilities 3. Performance issues 4. Readability and maintainability For each issue found, explain: what the problem is, why it matters, and provide a corrected code snippet. [PASTE CODE]
Debugging
You are a debugging expert for [LANGUAGE/FRAMEWORK]. I am encountering the following error: Error message: [PASTE ERROR] Context: [BRIEFLY DESCRIBE WHAT THE CODE DOES] Code: [PASTE RELEVANT CODE] List the most likely causes in order of probability. For the top cause, provide a step-by-step fix.
Writing Documentation
Write clear, concise developer documentation for the following function/module. Include: purpose, parameters (with types), return value, usage example, and any important edge cases. Format as JSDoc / docstring for [LANGUAGE]. [PASTE FUNCTION OR MODULE]
Architecture Decision
You are a software architect with expertise in scalable systems. I need to choose between the following approaches for [DESCRIBE PROBLEM]: Option A: [DESCRIBE OPTION A] Option B: [DESCRIBE OPTION B] My constraints: [TEAM SIZE, SCALE, BUDGET, EXISTING STACK] Compare the options on: maintainability, scalability, implementation complexity, and operational risk. Recommend one and justify your choice.
Refactoring
Refactor the following [LANGUAGE] code to improve readability and reduce complexity. Requirements: - Preserve all existing behaviour (do not change what the code does) - Follow [CODING STANDARD, e.g. "clean code principles" / "functional programming patterns"] - Add a brief comment for any non-obvious logic [PASTE CODE]
Writing Unit Tests
Write comprehensive unit tests for the following [LANGUAGE] function using [TEST FRAMEWORK]. Cover: happy path, edge cases, boundary conditions, and error cases. Mock any external dependencies. [PASTE FUNCTION]
Explaining Complex Code
Explain the following code to a developer who is unfamiliar with [CONCEPT/LIBRARY]. Use plain language. Avoid jargon where possible. Include: what it does step by step, why each part exists, and any potential pitfalls. [PASTE CODE]
Frequently Asked Questions
Why do some prompts produce better results than others?
Prompts that specify the desired output format, the model's role, and relevant constraints consistently produce better results. Vague prompts produce vague outputs. The more context you provide — language, framework, constraints, expected format — the more targeted the response.
Should I use a system prompt or user prompt for these templates?
For persistent behaviour across a session (e.g. always respond in TypeScript, always follow clean code principles), use a system prompt. For individual task-specific requests, use the user prompt. Many developers maintain a standing system prompt that sets the role and then use task-specific user prompts.
How do I reduce token usage with these prompts?
Remove unnecessary preamble. Instead of "Could you please help me to review the following code and provide suggestions?" write "Review this code. Identify bugs and suggest improvements." Same intent, roughly 60% fewer tokens.
Can I use these prompts with Claude or Gemini?
Yes. The templates here are model-agnostic. Claude tends to follow structured formatting instructions particularly well. Gemini performs well on code generation tasks. The core prompt structure applies across all major LLMs.
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Prompt Architect — Token Counter & Optimizer
Before sending a prompt to the API, measure its token count, strip unnecessary whitespace, and estimate the exact cost across GPT-4o, Claude, and Gemini.
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