AI Prompts for Coding: Ready-to-Use Templates and Best Practices

The most effective code generation prompt includes six elements: language and version, framework and conventions, function purpose, input types and examples, expected output with edge cases, and coding style preferences. When all six are present, the AI produces code that compiles, handles edge cases, and follows your team's conventions.

Template: You are a senior [language] developer. Write a function that [purpose]. It receives [inputs with types] and returns [output with type]. Handle these edge cases: [list]. Follow [style guide] conventions. Include JSDoc or docstring comments. Do not use any external libraries.

This template works across ChatGPT, Claude, Copilot, DeepSeek, and Replit. Adjust the specificity based on the complexity of the function. Simple utility functions need less context. Complex business logic functions need more.

Debugging prompts need the error context, the code, and the expected behavior. Without all three, the AI guesses about your situation and often suggests fixes for problems you do not have.

Template: I have a [language] [framework] application. The following function is throwing a [error type] error. Here is the error traceback: [traceback]. Here is the function code: [code]. The function should [expected behavior] when given [input example]. Identify the root cause, explain why it happens, and provide a minimal fix that preserves the existing behavior for all other inputs.

Adding what you have already tried prevents the AI from suggesting things you have ruled out. Debugging with AI is most efficient when you treat it as a colleague who needs to be caught up on the investigation so far.

Good code review prompts specify what to look for. Without this specification, you get surface-level comments about naming conventions when what you really need is a security audit.

Template: Review this [language] code for [focus areas: security, performance, readability, maintainability]. The code is part of a [project description] using [framework]. Our coding standards require [standards]. For each issue found, provide the line reference, explain why it is a problem, rate the severity as high, medium, or low, and suggest a specific fix.

For thorough reviews, break the focus areas into separate prompts. A security-focused review and a performance-focused review run separately will catch more issues than a single review that tries to cover everything at once.

Test generation is one of the highest-value AI coding tasks because writing tests is tedious but critical. A good test generation prompt includes the function code, the testing framework, and the types of tests you need.

Template: Write unit tests for the following [language] function using [testing framework]. Cover these scenarios: happy path with valid inputs, edge cases including [specific edge cases], error handling for invalid inputs, and boundary conditions. Use descriptive test names that explain what each test verifies. Mock [dependencies] if needed.

Always review generated tests for correctness. AI occasionally generates tests that pass but do not actually verify the behavior they claim to test. Run the tests with deliberately broken code to verify they catch real failures.

Documentation prompts should specify the audience, the documentation format, and the level of detail. API documentation is different from README documentation which is different from inline code comments.

Template: Write [documentation type] for the following [language] code. The audience is [audience]. Include: purpose, parameters with types, return value, usage examples, and error conditions. Use [format: JSDoc, docstring, markdown]. Keep the tone [technical but accessible].

For README files, include sections for installation, quick start, API reference, configuration, and contributing. Provide the project context so the AI understands what the user needs to know versus what is implementation detail.

Prompt God enhances coding prompts with language-specific context, framework conventions, and structured output format automatically. Save your best coding prompt templates in the workspace and reuse them across projects.

The Developer custom mode in Prompt God Pro optimizes enhancement for code correctness, edge case handling, and clean architecture. Combined with support for Replit, Bolt, Copilot, and all major AI tools, Prompt God covers your entire development workflow. Five free enhancements per day, unlimited with Pro.