30 Best Claude Prompts for Coding (Copy-Paste Ready 2026)

Claude’s reasoning depth makes it particularly strong for coding tasks that require understanding context, not just generating syntax. These 30 prompts are optimized for Claude’s strengths: debugging complex issues, architectural decisions, and thorough code review.

Debugging Prompts (1-8)

1. Systematic Bug Diagnosis

“I have a bug in [language/framework]. Expected behavior: [what should happen]. Actual behavior: [what’s happening]. Error message: [paste error]. Here’s the relevant code: [paste code]. Diagnose the likely cause, explain your reasoning, and provide a fix. If there are multiple possible causes, rank them by likelihood.”

2. Performance Debugging

“This [function/endpoint] is running slowly. It takes [X seconds] when it should take [Y seconds]. Here’s the code: [paste code]. Identify performance bottlenecks, explain why each is slow, and provide optimized alternatives. Include Big O analysis where relevant.”

3. Memory Leak Investigation

“I suspect a memory leak in this [language] application. Symptoms: [describe]. Here’s the relevant code: [paste code]. Identify potential memory leaks, explain why they occur, and provide fixes. Include patterns to prevent similar leaks.”

4. Race Condition Analysis

“I have a suspected race condition in this concurrent code: [paste code]. The bug manifests as [description]. It happens intermittently under [conditions]. Analyze the code for race conditions, explain the timing scenarios that cause them, and provide a thread-safe solution.”

5-8. More Debug Prompts

5. API integration debug: “This API call to [service] returns [error code]. Request: [paste request]. Expected response: [describe]. Here’s my code: [paste]. Diagnose the issue and fix it.”

6. Database query debug: “This SQL query returns unexpected results: [paste query]. Expected: [describe]. Actual: [describe]. Schema: [describe tables]. Fix the query and explain the issue.”

7. Build/deploy error: “I’m getting this error during [build/deploy]: [paste error]. Environment: [OS, versions]. Config: [paste relevant config]. Diagnose and provide step-by-step fix.”

8. Regex debugging: “This regex doesn’t match what I expect: Pattern: [regex]. Should match: [examples]. Shouldn’t match: [examples]. Fix the regex and explain each component.”

Code Review Prompts (9-14)

9. Security Review

“Review this code for security vulnerabilities: [paste code]. Check for: injection attacks, authentication flaws, data exposure, insecure dependencies, and OWASP Top 10 issues. For each finding: describe the vulnerability, show the exploit scenario, and provide the secure alternative.”

10. Production Readiness Review

“Review this code for production readiness: [paste code]. Check: error handling, logging, monitoring hooks, graceful degradation, configuration management, and scalability concerns. Rate readiness on a 1-10 scale with specific items to address.”

11-14. More Review Prompts

11. Clean code review: “Review this code for clean code principles: [paste code]. Check: naming, single responsibility, DRY, function length, complexity, and testability. Suggest specific refactoring steps.”

12. API design review: “Review this API design: [paste routes/schema]. Check: RESTful conventions, naming consistency, error response format, pagination, versioning, and documentation completeness.”

13. Database schema review: “Review this database schema: [paste schema]. Check: normalization, indexing strategy, naming conventions, data types, constraints, and potential query performance issues.”

14. Test coverage review: “Review these tests: [paste tests]. Check: edge cases covered, assertion quality, test isolation, naming clarity, and missing scenarios. The code being tested: [paste code].”

Architecture Prompts (15-20)

15. System Design

“Design a system for [requirement]. Constraints: [users, scale, budget, team size]. Provide: high-level architecture diagram (text-based), component breakdown, technology recommendations with justification, data flow, and potential failure points with mitigation strategies.”

16. Technology Selection

“I need to choose between [option A] and [option B] for [use case]. Requirements: [list]. Team: [size, experience]. Timeline: [duration]. Compare both options on: learning curve, performance, ecosystem, scalability, and long-term maintenance. Provide a clear recommendation with reasoning.”

17-20. More Architecture Prompts

17. Microservices breakdown: “I have a monolithic [language] application with these features: [list]. Help me plan a microservices migration: which services to extract first, communication patterns, data partitioning strategy, and migration timeline.”

18. Database selection: “I need a database for [use case]. Data characteristics: [describe]. Access patterns: [describe]. Scale: [numbers]. Compare SQL vs NoSQL options and recommend specific products with reasoning.”

19. Caching strategy: “Design a caching strategy for [application type]. Identify: what to cache, TTL strategy, invalidation approach, cache-aside vs write-through, and recommended cache technology.”

20. API versioning strategy: “Design an API versioning strategy for [API description]. Consider: backward compatibility, deprecation timeline, client migration support, and documentation approach.”

Testing Prompts (21-24)

21. Test Suite Generation

“Write comprehensive tests for this code: [paste code]. Include: unit tests for each function, edge cases (null, empty, boundary values), error scenarios, and integration test outlines. Use [testing framework]. Follow AAA pattern (Arrange, Act, Assert).”

22-24. More Testing Prompts

22. Test data generation: “Generate test data for [describe domain]. Include: 10 valid records, 5 edge cases, and 5 invalid records with explanations of why they’re invalid. Format as [JSON/SQL/fixtures].”

23. Load test plan: “Create a load testing plan for [API/application]. Define: scenarios, user patterns, ramp-up strategy, success criteria, and k6/JMeter script outline.”

24. E2E test scenarios: “Write end-to-end test scenarios for [feature]. Include: happy path, error paths, edge cases, and user workflow variations. Format as Gherkin (Given/When/Then).”

Documentation Prompts (25-27)

25. API Documentation

“Generate API documentation for these endpoints: [paste routes/code]. Include: endpoint description, request parameters, request body schema, response schema, error codes, authentication requirements, and curl examples. Format as OpenAPI/Swagger.”

26-27. More Documentation Prompts

26. README generation: “Generate a comprehensive README.md for [project]. Include: description, features, installation, configuration, usage examples, API reference, contributing guidelines, and license.”

27. Code comments: “Add clear, concise comments to this code: [paste code]. Comment WHY, not WHAT. Focus on non-obvious logic, business rules, and gotchas. Don’t comment self-evident code.”

Learning Prompts (28-30)

28. Technology Deep Dive

“Explain [technology/concept] to me as a [experience level] developer. Cover: what it is, why it exists, when to use it (and when not to), how it works internally, and a practical example I can build. Include common pitfalls.”

29. Code Translation

“Convert this [source language] code to [target language]: [paste code]. Maintain the same logic but use idiomatic [target language] patterns. Explain any differences in approach between the languages.”

30. Design Pattern Application

“Show me how to apply the [pattern name] design pattern to solve this problem: [describe problem]. Provide: the pattern explanation, why it fits this problem, a complete implementation in [language], and when this pattern would be overkill.”

Tips for Better Claude Coding Prompts

• Include the full context: Claude handles long inputs well. Paste complete files rather than snippets
• Specify the language and framework: Claude adapts its suggestions to your specific stack
• Ask for reasoning: Claude’s explanations help you learn, not just get answers
• Request trade-offs: Ask Claude to explain the downsides of its suggestions

For a comparison of AI coding tools, see best AI for coding and Copilot vs Cursor.

Frequently Asked Questions

Is Claude better than ChatGPT for coding?

Claude excels at debugging, code review, and architectural decisions requiring deep reasoning. ChatGPT is faster for quick code generation and has better plugin integration. See our comparison.

Can Claude write production-ready code?

Claude generates high-quality code, but all AI-generated code requires human review. Claude is particularly good at flagging potential issues in its own output.

Which Claude model is best for coding?

Claude Opus (available on Pro plan) produces the highest quality code and most thorough analysis. Claude Sonnet is faster and sufficient for routine coding tasks.