ChatGPT Prompts for Coding: 50 Developer Prompts That Actually Work
50 ChatGPT prompts for coding that developers actually use daily — debugging, code review, architecture, documentation, and learning new languages fast.
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A senior backend engineer I know hit a particularly nasty concurrency bug on a Friday afternoon. Two hours in, he pasted the relevant code into ChatGPT with a specific question about the race condition. It identified the exact issue — a lock being acquired in inconsistent order across two goroutines — in the first response. He said he would have spent another two hours without it.
That's not magic. It's what happens when you give an AI model a precise question with enough context. Most developers don't do this. They paste error messages and get generic answers. The prompts in this guide are written to extract the specific, technical responses that are actually useful.
How Developers Should Think About ChatGPT
ChatGPT is not an IDE plugin (that's Copilot's job). It's closer to a very fast, very available senior engineer who happens to know a lot about a lot of languages — but who hasn't seen your codebase and sometimes confidently says wrong things.
Use it for:
- Debugging with full context
- Code review with specific focus areas
- Architecture discussion
- Writing tests for existing code
- Learning unfamiliar patterns or languages
- Documentation you'd otherwise avoid writing
Don't trust it blindly for:
- Security-sensitive code
- Library-specific APIs (it may use outdated versions)
- Performance-critical code without benchmarking
- Anything involving cryptography
The developer tools page has a comparison of AI coding tools if you want to see how ChatGPT stacks up against dedicated code tools in 2026.
The Prompt Workflow for Coding Tasks
Section 1: Debugging Prompts (1–12)
Error Messages
I'm getting this error in [language] [version]:
[error message]
Here's the relevant code:
[code block]
I've already tried: [what you attempted]
What's causing this and what's the fix? If there are multiple possible causes, list them in order of likelihood.
This code should [expected behavior] but instead it [actual behavior]. No error is thrown — it just silently does the wrong thing.
[code block]
Walk me through the logic step by step and identify where it diverges from what I expect.
Logic Errors
I have a function that processes [what it does] but it fails on edge cases. Here's the function:
[code block]
What edge cases am I not handling? For each one, show me: (1) the input that triggers it, (2) what happens, (3) how to fix it.
This SQL query is returning duplicate rows and I can't figure out why:
[query]
Schema:
[relevant table definitions]
What's causing the duplicates? Show the corrected query.
Performance Issues
This function is running much slower than expected on large datasets. Language: [language]. Dataset size: [size].
[code block]
Profile this mentally — where are the likely bottlenecks? Suggest optimizations in order from highest to lowest expected impact.
I have an N+1 query problem in my [ORM/framework]. Here's the relevant code:
[code]
Show me how to fix it. Explain the optimized version so I understand what changed.
Section 2: Code Review Prompts (13–22)
Asking for a "code review" without specifying what kind produces generic output. These prompts target specific review dimensions.
Security Review
Review this code for security vulnerabilities. Language: [language]. This code handles [user input / authentication / database queries / file uploads — specify].
[code block]
List every security issue you find, ranked by severity. For each: explain the vulnerability, show a minimal exploit example if relevant, and show the fix.
Check this authentication flow for security issues:
[code]
I'm specifically worried about: session management, token expiry, and brute force protection. What am I missing?
Performance Review
Review this for performance. Focus on: algorithmic complexity, unnecessary allocations, and database query efficiency. Tech stack: [stack].
[code block]
Be specific — don't just say "this could be faster." Tell me what the current complexity is and what it could be.
Readability and Maintainability
Review this code for readability and maintainability. The person maintaining this will be a mid-level developer unfamiliar with this codebase.
[code block]
What will confuse them? What should be renamed, extracted, or documented? Show the refactored version side-by-side.
Code Review Comparison Table
| Review Type | What to Specify in Prompt | Common Findings |
|---|---|---|
| Security | Input type, auth method, data sensitivity | Injection flaws, missing validation, insecure defaults |
| Performance | Dataset size, frequency of execution | N+1 queries, unnecessary loops, missing indexes |
| Readability | Reader's experience level | Unclear naming, missing comments, too-long functions |
| Architecture | System scale, team size | Missing abstraction, tight coupling, violation of SRP |
| Testing | Current coverage, critical paths | Missing edge cases, brittle tests, no error path coverage |
Section 3: Writing Code From Scratch (23–33)
Feature Implementation
I need to implement [feature description] in [language/framework].
Requirements:
- [requirement 1]
- [requirement 2]
- [requirement 3]
Constraints: [performance requirements, library restrictions, team conventions]
Give me: (1) the implementation, (2) a brief explanation of the approach, (3) what edge cases I need to test.
Write a [utility function / API endpoint / data model] that does the following:
[description]
Tech stack: [language, framework, relevant libraries]
Style: follow [PEP 8 / Google style / our team uses camelCase — whatever applies]
Include: type hints / JSDoc comments / error handling
Data Structures and Algorithms
I need to implement [data structure or algorithm] from scratch in [language]. I understand the concept but I've never implemented it.
Walk me through building it step by step. After each step, pause and explain why we're doing it that way — not just what.
I have a list of [data] and I need to [operation — sort by X, find duplicates, group by Y, etc.]. Most efficient approach in [language]?
Show me: (1) the brute force approach with its complexity, (2) the optimal approach with its complexity, (3) when I'd choose one over the other.
API Integration
I need to integrate with the [API name] API to [what you're doing].
Here's the documentation for the relevant endpoint:
[paste docs or describe endpoint]
Write the integration code in [language]. Include: authentication, error handling, rate limit awareness, and a simple retry on 429/503.
Section 4: Testing Prompts (34–40)
Testing is the task developers skip most. These prompts make it faster.
Write unit tests for this function:
[code]
Testing framework: [pytest / Jest / go test / etc.]
Cover: happy path, edge cases, and error conditions.
For each test, add a one-line comment explaining what scenario it covers.
I have this function and these existing tests:
[code + tests]
What cases am I not testing? List the gaps and write the missing tests.
Generate integration test scenarios for this API endpoint:
[endpoint code or description]
I need to test: normal flow, auth failure, invalid input, database unavailable, and concurrent requests. Give me test pseudocode I can implement in [framework].
Write property-based tests for this function using [Hypothesis / fast-check / QuickCheck]:
[function code]
Define the properties that should hold for all valid inputs and generate the test code.
Section 5: Architecture and System Design (41–45)
I'm designing a system that needs to [describe what the system does]. Expected scale: [users, requests/second, data volume].
What architectural patterns should I consider? For each option, give me: pros, cons, and the scenario it fits best. Don't just list patterns — tell me which one you'd lean toward for my use case and why.
Review this architecture diagram / description and identify: single points of failure, scalability bottlenecks, and anything that would be painful to change later.
[describe or paste architecture]
I'm choosing between [Approach A] and [Approach B] for [specific technical decision]. My constraints are: [team size, scale, timeline, existing stack].
Give me a direct recommendation. I don't want a list of tradeoffs without a conclusion — tell me which one to pick and why.
Section 6: Documentation and Explanation (46–50)
Documentation is where most developers take the biggest shortcut. These prompts make it painless.
Write documentation for this function. Audience: developers who will maintain this code but didn't write it.
[code]
Include: what it does, parameters with types and valid ranges, return value, exceptions it can throw, and one usage example.
Explain this code to me as if I'm a [junior developer / developer coming from Python / non-technical stakeholder]:
[code]
Use an analogy if the concept is abstract. Don't skip anything — I'd rather a longer explanation than one that assumes knowledge.
I need to write a README for this project:
[describe the project, its purpose, tech stack]
Write a README with: project description, prerequisites, installation steps, usage examples, and configuration options. Keep it practical — I don't want a marketing document.
Prompts That Work vs. Prompts That Don't
| Weak Prompt | Strong Version | Why It Works Better |
|---|---|---|
| "Fix my code" | "This function returns wrong output for negative inputs. Here's the code and test cases." | Specific failure condition |
| "Is this good code?" | "Review this for readability. Reader is a mid-level dev unfamiliar with this repo." | Defined review scope and audience |
| "Explain recursion" | "Explain recursion to someone who knows loops but has never used recursion. Use a tree traversal example." | Background context + concrete example requested |
| "Write tests" | "Write Jest tests for this function, covering error paths and the edge case where input is an empty array." | Specific test types and edge case |
| "What's wrong?" | "Getting TypeError on line 42. Here's the stack trace, the function, and what input triggers it." | Full diagnostic context |
Learning New Languages Fast With ChatGPT
One genuinely underused application: using ChatGPT to accelerate language learning. The role prompting techniques guide explains this pattern in depth — you can have it play the role of a language expert comparing patterns between languages.
A practical approach:
I know Python well. I'm learning Rust. For each of these patterns, show me the Python version I know and the idiomatic Rust equivalent. Explain what's different and why Rust does it that way:
- Iterating over a list
- Error handling
- Creating a struct with methods
- Reading a file line by line
- Concurrency with threads
This is far more efficient than reading documentation linearly. You're mapping new syntax to existing mental models rather than starting from scratch.
For a deeper understanding of how language models process and generate code, the LLM Concepts Notes has relevant background on tokenization and context limits that affects how you structure long code prompts.
Want to test your prompting skills? The Prompt Engineering Basics Quiz covers the fundamentals that apply to coding prompts specifically.
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