50 AI Prompts for Developers: Debug, Refactor, and Ship Faster with ChatGPT, Claude & Gemini
Introduction
The average developer spends 35-50% of their time not writing new code but debugging, reviewing, refactoring, and documenting existing code [1]. AI coding assistants have compressed many of these tasks from hours to minutes, yet most developers still approach them the same way: open a chat window, type a vague request, get a mediocre answer, close the tab. The prompt disappears. The next time the same problem surfaces, the cycle restarts from zero.
This is a workflow problem, not a tooling problem. The developers who extract the most value from ChatGPT, Claude, and Gemini are not the ones who write prompts from scratch every time. They are the ones who maintain a library of tested, reusable prompts, each tuned for a specific task in their development workflow [2]. A 2025 GitHub survey found that developers using AI tools reported a 55% faster task completion rate, but the variance was enormous: those with structured prompt habits outperformed ad hoc users by a factor of 2.3x [3].
This article provides 50 ready-to-use prompts organized by developer workflow category. Each prompt uses [bracket placeholders] so you can drop in your own context. They work across ChatGPT, Claude, and Gemini, with notes where a specific model excels. Copy what you need, adapt it, and save the ones that work into a personal prompt library so they compound in value over time.
50 AI prompts for developers organized by workflow: debugging, code review, refactoring, testing, documentation, architecture, DevOps, and learning
1. Debugging & Error Resolution
When a stack trace fills your terminal and the error message reads like an ancient riddle, these prompts turn AI into a diagnostic partner.
1. Decode a cryptic error message
I'm getting this error in my [language/framework] project: [paste error]. Explain what is causing it, why it happens, and suggest 3 possible fixes ranked by likelihood. Include code examples for each fix.
2. Trace a bug through multiple files
I have a bug where [describe unexpected behavior]. The relevant files are: [paste file names or code snippets]. Walk through the execution flow step by step and identify where the behavior diverges from the expected result.
Claude excels here when you paste entire files; its 200K context window handles large codebases without truncation.
3. Reproduce a race condition
I suspect a race condition in my [language] application. [Describe the intermittent behavior]. Here is the relevant concurrent code: [paste code]. Identify potential race conditions, explain the timing that triggers them, and suggest synchronization fixes.
4. Debug a memory leak
My [language/framework] application's memory usage grows steadily over time. Here is the relevant code: [paste code]. Identify potential memory leaks, explain why each one causes retained memory, and provide fixed versions.
5. Fix a failing API integration
My application is getting [status code] errors when calling [API name]. Here is my request code: [paste code]. Here is the error response: [paste response]. Diagnose the issue and provide a corrected implementation with proper error handling.
6. Diagnose a performance bottleneck
This function takes [X seconds] to execute when processing [Y records]: [paste code]. Profile the algorithmic complexity, identify the bottleneck, and suggest optimizations with their expected time complexity improvements.
7. Resolve dependency conflicts
My [package manager] is throwing this dependency conflict: [paste error]. Explain which packages are conflicting, why the versions are incompatible, and provide the safest resolution that does not introduce breaking changes.
2. Code Review & Quality
Use these before submitting a PR, or paste in a colleague's code to prepare informed review comments.
8. Comprehensive code review
Review this [language] code for bugs, security vulnerabilities, performance issues, and adherence to best practices. For each issue found, explain the severity (critical/major/minor), the risk, and provide a corrected version. Code: [paste code]
9. Security audit
Perform a security audit of this [language/framework] code. Check for: injection vulnerabilities, authentication/authorization flaws, data exposure risks, insecure defaults, and missing input validation. Code: [paste code]
For security reviews, consider running the same prompt through both Claude and ChatGPT and comparing results. Different models catch different vulnerability classes.
10. Check for edge cases
Analyze this function for edge cases that could cause unexpected behavior: [paste code]. List every edge case you can identify, explain what would happen for each, and suggest defensive code to handle them.
11. Evaluate error handling
Review the error handling in this code: [paste code]. Identify: silent failures, overly broad catch blocks, missing error types, unhandled promise rejections, and any error that would be difficult to diagnose in production. Suggest improvements.
12. Assess API design
Review this REST/GraphQL API design: [paste endpoint definitions or schema]. Evaluate it for: consistency, proper HTTP semantics, versioning strategy, pagination approach, error response format, and backward compatibility. Suggest improvements.
13. Review database queries
Analyze these database queries for performance and correctness: [paste queries]. Check for: missing indexes, N+1 query patterns, unnecessary joins, potential deadlocks, and SQL injection risks. Suggest optimized alternatives.
14. Naming and readability review
Review this code purely for readability and naming conventions: [paste code]. Suggest better names for variables, functions, and classes. Identify any sections where the intent is unclear and suggest clarifying rewrites or comments.
Want to know how effective your prompts are? Prompt Score analyzes them on 6 criteria.
These prompts help you improve existing code without changing its behavior.
15. Extract a reusable abstraction
This code has repeated patterns: [paste code with duplication]. Identify the common pattern, extract it into a reusable [function/class/hook/module], and show how each call site would use the new abstraction.
16. Simplify complex conditionals
Simplify this conditional logic without changing its behavior: [paste code with nested if/else or complex boolean expressions]. Use early returns, guard clauses, or lookup tables where appropriate. Show the before and after.
17. Modernize legacy code
Refactor this [old language version] code to use modern [language/version] features: [paste code]. Preserve identical behavior. Use [specific features, e.g., async/await, destructuring, pattern matching]. Explain each change and why the modern version is better.
18. Reduce function complexity
This function has a cyclomatic complexity that is too high: [paste code]. Break it into smaller, single-responsibility functions. Each extracted function should have a clear name that describes its purpose. Show the refactored result with all functions.
19. Optimize a hot path
This code runs in a performance-critical path ([describe context, e.g., "called 10K times per request"]): [paste code]. Optimize it for speed. Consider: algorithmic improvements, caching, avoiding allocations, and data structure choices. Benchmark the expected improvement.
20. Convert callback-based code to async/await
Convert this callback-based [language] code to use async/await: [paste code]. Handle all error paths. Ensure the converted version maintains identical behavior, including error propagation and concurrent operations.
21. Apply a design pattern
This code would benefit from a design pattern: [paste code]. Suggest the most appropriate pattern ([Strategy/Observer/Factory/etc.] or recommend one), explain why it fits, and show the refactored implementation.
4. Testing & Test Generation
Writing tests is the task developers most frequently delegate to AI. These prompts generate tests that actually catch bugs.
22. Generate unit tests
Write comprehensive unit tests for this function: [paste code]. Use [testing framework, e.g., Jest, pytest, JUnit]. Cover: happy path, edge cases, error conditions, and boundary values. Each test should have a descriptive name that explains what it verifies.
23. Generate integration tests
Write integration tests for this [API endpoint/service interaction]: [paste code or API spec]. Test: successful operations, error responses, authentication, input validation, and concurrent access. Use [testing framework] with [mocking library if applicable].
24. Test edge cases systematically
For this function: [paste code], generate a test matrix covering: null/undefined inputs, empty collections, single-element collections, maximum-size inputs, special characters, boundary values for numeric parameters, and type coercion scenarios. Use [testing framework].
25. Create test fixtures and factories
Create test fixtures/factories for these data models: [paste types or schema]. Generate realistic but deterministic test data. Include: a factory function with sensible defaults, overrides for each field, and helper functions for common test scenarios. Use [testing framework/library].
26. Write property-based tests
Write property-based tests for this function: [paste code]. Identify the invariants that should hold for all valid inputs. Use [property-based testing library, e.g., fast-check, Hypothesis, QuickCheck]. Include at least 5 properties.
Gemini handles large test generation tasks well because it can process extensive codebases and produce voluminous structured output without degrading.
27. Mock external dependencies
Create mocks for these external dependencies used in my tests: [paste interfaces or function signatures]. The mocks should: track call history, support configurable return values, simulate error conditions, and have TypeScript/type-safe interfaces. Use [mocking library].
28. Generate regression tests from a bug report
A bug was reported: [describe the bug and reproduction steps]. Write regression tests that: reproduce the original bug (should fail before fix), verify the correct behavior (should pass after fix), and cover related edge cases that might have the same root cause. Use [testing framework].
5. Documentation & Comments
AI generates first drafts of documentation faster than any human, but the quality depends entirely on the prompt.
29. Write function documentation
Write documentation for this function: [paste code]. Include: a one-line summary, parameter descriptions with types and constraints, return value description, exceptions/errors that can be thrown, a usage example, and any important side effects or caveats. Use [documentation format, e.g., JSDoc, docstring, XML comments].
30. Generate a README for a module
Generate a README.md for this module: [paste main file or describe the module]. Include: purpose, installation, quick start example, API reference for the main exports, configuration options, common pitfalls, and a contributing section. Target audience: [junior developers / senior engineers / external API consumers].
31. Write inline comments for complex logic
Add inline comments to this code to explain the non-obvious logic: [paste code]. Do not comment on what the code does (the code itself shows that). Comment on WHY: business rules, edge case handling, performance trade-offs, and assumptions. Keep comments concise.
32. Create an architecture decision record (ADR)
Write an ADR for this decision: we chose [option A] over [option B] for [system component]. Context: [describe the problem]. Include: title, status (proposed/accepted), context, decision, consequences (positive and negative), and alternatives considered with reasons for rejection.
The techniques you're reading about work. Test your prompts now with Prompt Score and see your score in real time.
Write API documentation for this endpoint: [paste route handler or describe the endpoint]. Include: HTTP method, URL pattern, request headers, request body schema with examples, response schemas for success and each error case, rate limiting details, and authentication requirements. Use [format: OpenAPI/Markdown/etc.].
34. Generate a changelog entry
Based on these code changes: [paste diff or describe changes], write a changelog entry following [Keep a Changelog / Conventional Commits] format. Categorize changes under: Added, Changed, Deprecated, Removed, Fixed, Security. Write from the user's perspective, not the developer's.
6. Architecture & Design Decisions
These prompts work best when you provide enough context about your constraints. The more specific you are about scale, team size, and requirements, the more actionable the response.
35. Evaluate a technical approach
I need to implement [feature/system]. I'm considering these approaches: [approach A] and [approach B]. My constraints are: [list constraints, e.g., team of 3, must ship in 2 weeks, expected traffic of 10K RPM, must integrate with existing [system]]. Compare the approaches across: implementation complexity, scalability, maintainability, and risk. Recommend one with reasoning.
36. Design a database schema
Design a database schema for [describe the domain]. Requirements: [list requirements]. Expected data volumes: [describe scale]. Access patterns: [describe main queries]. Include: table definitions, indexes, relationships, and explain your normalization choices. Use [PostgreSQL/MySQL/MongoDB].
Claude is particularly strong at generating and reasoning about database schemas when given detailed domain context.
37. Plan a migration strategy
I need to migrate from [current system/architecture] to [target system/architecture]. Current state: [describe]. Constraints: [zero downtime / limited team / timeline]. Design a phased migration plan with: steps in order, rollback strategy for each step, data migration approach, testing checkpoints, and risk mitigation.
38. Design an API contract
Design the API contract for a [describe service/feature]. Consumers: [list consuming services or clients]. Requirements: [list requirements]. Include: endpoint definitions, request/response schemas, error codes, pagination strategy, versioning approach, and authentication method. Consider backward compatibility.
39. Evaluate build vs. buy
I'm deciding whether to build or buy a solution for [describe need]. Requirements: [list]. Team context: [size, expertise]. Budget: [range]. Timeline: [deadline]. Compare: building a custom solution vs. [specific product/service]. Include: total cost of ownership over 2 years, maintenance burden, flexibility, vendor lock-in risk, and time to first value.
40. Plan a monolith-to-services decomposition
Here is our monolith's module structure: [paste or describe]. I want to extract [module] into a separate service. Describe: where to draw the service boundary, how to handle shared data, the communication pattern (sync/async), the strangler fig migration steps, and what to watch out for in terms of distributed system complexity.
7. Git & DevOps
Prompts for the tasks that surround coding: version control, CI/CD, deployment, and infrastructure.
41. Write a meaningful commit message
Based on this diff: [paste diff], write a commit message following [Conventional Commits / your team's format]. The subject line should be under 72 characters. The body should explain WHY the change was made, not just WHAT changed. Include any relevant issue references.
42. Resolve a merge conflict
I have a merge conflict in this file. Here are both versions: [paste conflict markers with both sides]. The base branch intent was: [describe]. My branch intent was: [describe]. Suggest the correct resolution that preserves both intents, and explain your reasoning.
43. Write a CI/CD pipeline
Write a [GitHub Actions / GitLab CI / Jenkins] pipeline for a [language/framework] project. Requirements: run linting, run tests, build the project, deploy to [environment]. Include: caching for dependencies, parallel test execution, conditional deployment (only on main branch), and failure notifications.
44. Debug a failing pipeline
My CI/CD pipeline is failing with this output: [paste log]. The pipeline config is: [paste config]. Identify the failure cause and suggest a fix. If the failure is flaky, suggest how to make it deterministic.
45. Write a Dockerfile
Write an optimized Dockerfile for a [language/framework] application. Requirements: [list, e.g., multi-stage build, non-root user, minimal image size, health check]. Include comments explaining each layer choice and a .dockerignore file. Target base image: [or recommend one].
8. Learning & Exploration
Use AI not just to solve today's problem but to deepen your understanding. These prompts turn AI models into patient technical mentors.
46. Explain a concept with progressive depth
Explain [concept, e.g., event sourcing, CRDT, WebAssembly] at three levels: (1) a one-paragraph overview for someone who has never heard of it, (2) a technical explanation with a code example for a mid-level developer, (3) an in-depth analysis of trade-offs, edge cases, and when NOT to use it for a senior engineer.
Gemini's large context window makes it well suited for explore-and-learn sessions where the conversation spans many follow-up questions.
47. Compare technologies for a specific use case
Compare [technology A] vs [technology B] for [specific use case]. Evaluate: performance characteristics, learning curve, ecosystem maturity, community size, hiring pool, long-term maintenance, and total cost. Provide a recommendation based on: [your constraints, e.g., small team, startup, enterprise].
48. Walk through an algorithm
Walk through [algorithm name] step by step using this example input: [provide input]. Show the state at each step. Then explain: the time and space complexity, when to use it over alternatives, and common implementation pitfalls. Write a clean implementation in [language].
49. Analyze an open source codebase pattern
Explain how [open source project, e.g., React, PostgreSQL, Redis] implements [specific feature or pattern]. Describe the high-level architecture, the key design decisions, and why they made those choices. What can I learn from this for my own code?
50. Build a mental model for a new domain
I'm starting to work with [new domain, e.g., real-time systems, distributed databases, ML pipelines]. Create a learning roadmap: the 5 most important concepts to understand first, recommended resources for each, common misconceptions, and a small hands-on project that would exercise the key concepts.
Tips for Getting Better Results from These Prompts
The 50 prompts above are starting points, not finished products. The developers who see the best results treat prompts the same way they treat code: as artifacts that improve through iteration and context [4]. Here are four principles for adapting them.
Always specify your stack
Replace [language/framework] with your actual technology. "Review this code" produces generic advice. "Review this TypeScript/Next.js 16 code using App Router with server components" produces specific, actionable feedback. Every layer of specificity you add removes a layer of ambiguity from the response.
Include relevant constraints
AI models cannot read your mind. If your function must handle 100K requests per second, say so. If the refactoring must not break the public API, say so. Constraints are what turn "theoretically correct" advice into "actually usable in my project" advice. The TCOF framework provides a structured way to think about adding context.
Use system prompts for persistent context
If you regularly work in the same codebase, set a system prompt (sometimes called "custom instructions") with your tech stack, coding conventions, and preferences. Every prompt you send inherits that context automatically. This is particularly powerful with Claude's Projects feature and ChatGPT's custom instructions. For more on what makes a good system prompt, see our guide on prompt scoring.
Iterate, do not accept the first output
The first response is a draft. Ask follow-up questions: "What edge cases did you miss?", "How would this change at 10x scale?", "Rewrite this to be more idiomatic [language]." The iterative loop is where the real value emerges. Track which prompt versions work best and save the refined versions. Effective prompt writing is a skill that compounds with practice.
Choose the right model for the task
Not all models are interchangeable. Claude handles long code context and nuanced reasoning particularly well. ChatGPT is strong at broad knowledge synthesis and creative generation. Gemini excels with very large context windows and multimodal inputs. Matching the model to the task type can be the difference between a useful answer and a mediocre one.
How to get better results from AI coding prompts: add context, specify constraints, iterate on output
From 50 Prompts to a Living Library
Copying these prompts into a notes app is a start. Building a system around them is what produces lasting results. The developers who get the most from AI coding assistants are the ones who treat their prompts like they treat their code: organized, versioned, and continuously improved [5].
A prompt library turns one-off experiments into compounding assets. When you find a debugging prompt that works for your React codebase, save it with the right category and tags. When you refine it three months later, save the new version so you can compare. When your team adopts it, share it in a workspace where everyone benefits. This is the trajectory from individual productivity to organizational prompt infrastructure.
Keep My Prompts was built for exactly this workflow. Save your best prompts, organize them by project or workflow, track versions as you refine them, and score them with AI to identify what is working and what needs improvement. It works with every model because your prompts are yours, not locked to any vendor's ecosystem.
Start with the 5 prompts from this list that match your daily work. Save them, use them for a week, refine what needs refining, and build from there. Your prompt library, like your codebase, gets better with every commit.
[1] Minelli, R., Mocci, A., & Lanza, M. (2015). "I Know What You Did Last Summer: An Investigation of How Developers Spend Their Time." IEEE 23rd International Conference on Program Comprehension (ICPC), 25-35.
[2] White, J., et al. (2023). "A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT." arXiv preprint arXiv:2302.11382.
[3] GitHub. (2025). "The State of AI in Software Development: Developer Productivity Survey." GitHub Blog.
[4] Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models." Advances in Neural Information Processing Systems (NeurIPS), 35, 24824-24837.
[5] Ebert, C., & Louridas, P. (2023). "Generative AI for Software Development." IEEE Software, 40(6), 110-117.
