ChatGPT is one of the most capable AI coding assistants available — it handles Python, JavaScript, TypeScript, SQL, and dozens of other languages with confidence. But developers who get the most out of it share one trait: they give ChatGPT enough context. The model's output quality scales directly with how much you tell it about your stack, your constraints, and what 'good' looks like for your codebase.
These prompts are structured for real development tasks — not toy examples. Each one gives ChatGPT the context it needs to produce code you'd actually commit.
I have a bug in the following [LANGUAGE] code. The expected behavior is [EXPECTED]. The actual behavior is [ACTUAL]. Error message (if any): [ERROR]. Here is the relevant code: [PASTE CODE] Identify the root cause, explain why it's happening, and provide a fixed version. If there are other potential issues in the code beyond the bug I described, flag them briefly.
Returns root cause explanation + fixed code + any additional issues spotted.
Review the following [LANGUAGE] code. Check for: (1) bugs or logic errors, (2) performance issues, (3) security vulnerabilities, (4) code style and readability, (5) edge cases not handled. Be specific — reference line numbers or function names. Provide concrete suggestions, not just observations. Here is the code: [PASTE CODE]
Returns a structured review with actionable fixes, not vague feedback.
Write a [LANGUAGE] function that [WHAT IT SHOULD DO]. Inputs: [INPUTS WITH TYPES]. Output: [OUTPUT WITH TYPE]. Constraints: [CONSTRAINTS]. Error handling: [HOW TO HANDLE ERRORS]. Include: the function, a brief docstring, and 3 unit tests. Use [STYLE: idiomatic/minimal/well-commented] code style.
Returns a complete function with tests — ready to adapt and integrate.
Refactor the following [LANGUAGE] code to improve readability and maintainability without changing behavior. Apply: better variable names, extracted helper functions where appropriate, reduced nesting, cleaner control flow. Show before and after. Explain the key changes. [PASTE CODE]
Returns a cleaner version with an explanation of each structural change.
Explain the following [LANGUAGE] code as if I'm a [LEVEL: junior/mid-level/senior] developer who has never seen this pattern before. Cover: what it does, how it works step by step, why each key decision was made, and any gotchas or edge cases I should be aware of. [PASTE CODE]
Returns a clear explanation tailored to your experience level.
Write a SQL query to [WHAT YOU WANT TO ACHIEVE]. Database: [MySQL/PostgreSQL/SQLite/etc]. Tables involved: [TABLE NAMES AND RELEVANT COLUMNS]. Constraints: [FILTERS, JOINS, AGGREGATIONS]. Performance requirement: [IF ANY]. Include: the query, a brief explanation of the logic, and one alternative approach if applicable.
Returns a working query with explanation — test against your schema.
Write [LANGUAGE] code to integrate with the [API NAME] API. Endpoint: [ENDPOINT]. Method: [GET/POST/etc]. Required headers/auth: [AUTH TYPE]. Request body (if POST): [SCHEMA]. Expected response: [SCHEMA]. Handle: network errors, rate limiting, and invalid responses. Use [LIBRARY: axios/fetch/requests/etc].
Returns production-ready API integration code with error handling.
Write unit tests for the following [LANGUAGE] function using [TEST FRAMEWORK: Jest/Pytest/etc]. Cover: (1) happy path, (2) edge cases (empty inputs, boundary values), (3) error cases. Each test should have a clear description. Aim for 100% branch coverage. Here is the function: [PASTE FUNCTION]
Returns a test suite covering happy paths, edges, and error cases.
The following [LANGUAGE] code is too slow. Current performance: [WHAT'S SLOW]. Constraints: I cannot change the interface/API. Suggest optimizations in order of impact. For each suggestion: explain the issue, show the optimized code, and estimate the performance gain. [PASTE CODE]
Returns prioritized optimizations with code — implement in order of impact.
I'm building [WHAT YOU'RE BUILDING] in [LANGUAGE/FRAMEWORK]. I need to decide between [OPTION A] and [OPTION B] for [SPECIFIC DECISION]. My constraints: [SCALE/TEAM SIZE/DEADLINE/EXISTING STACK]. Evaluate both options: pros, cons, when each is appropriate. Give a recommendation with reasoning. Don't be neutral — pick one.
Returns a concrete recommendation with trade-off analysis — not a 'it depends' non-answer.
Copy any prompt above and run it through all three AI models simultaneously in MultiLLM. See which gives the best answer for your exact use case.
The best coding prompts give ChatGPT your language, framework, expected behavior, and constraints explicitly. For debugging, include the error message. For code generation, include input/output types. For reviews, ask for specific categories (bugs, performance, security).
ChatGPT is strong for code generation, SQL, and common patterns. Claude often produces better output for complex TypeScript, architecture decisions, and tasks that require reasoning through a problem before writing code. Use MultiLLM to compare both on your hardest coding problems.
Yes, with review. ChatGPT produces solid production code but always review for edge cases, security issues, and codebase-specific patterns it can't know about. Use it to accelerate development, not replace code review.
Paste an example of code from your codebase and say 'write in this style and follow these conventions.' Specify your preferences explicitly: error handling patterns, naming conventions, comment style.