Coding Prompt Examples
These are illustrative example prompts for coding tasks with ChatGPT, Claude, or other AI models. Structured coding prompts reduce ambiguity and help AI models produce more accurate, usable code. These are example prompts — not user testimonials.
These are illustrative example prompts, not user testimonials.
Code generation (TAG framework)
Task: Python function to process CSV files Action: Write a function that reads a CSV file, filters rows where the 'status' column equals 'active', and returns the filtered data as a list of dictionaries Goal: Clean, production-ready Python with type hints, docstring, error handling for missing file or missing column, and a brief usage example in the docstring
What makes it effective: The Task defines what, the Action defines exact behavior including edge cases (missing column), and the Goal specifies code quality standards — reducing back-and-forth revisions.
Debugging (RISE framework)
Role: Experienced Python developer with expertise in async programming Input: The following Python async function is throwing a RuntimeError: "This event loop is already running" — [user would paste code here] Steps: 1) Identify the root cause, 2) Explain why this error occurs in plain language, 3) Provide the corrected code Execution: Show the corrected code first, then the explanation. Include inline comments on changed lines.
What makes it effective: The RISE framework (Role, Input, Steps, Execution) ensures the AI diagnoses before fixing, explains the fix, and formats the output with the most useful part — corrected code — at the top.
Code review (ROLE framework)
Role: Senior software engineer specializing in code quality and security Objective: Review the following JavaScript function for bugs, security issues, and code quality problems Limitations: Focus on the function logic only, not file structure or project setup. Do not rewrite the entire function unless necessary. Expectations: Numbered list of issues found, severity (Critical/Major/Minor), specific line references, and suggested fixes
What makes it effective: Scoping the review to function logic only prevents sprawling feedback. Requiring severity labels and line references makes the output directly usable as a review checklist.
Documentation writing (COAST framework)
Context: Open-source Python library for data validation Objective: Write README documentation for the following module [user would paste module code] Actions: Write Overview, Installation, Quick Start with code example, API reference for public functions, Contributing section Scenario: Target audience is Python developers with intermediate experience. GitHub README format using Markdown. Task: Complete README.md content ready to paste into GitHub
What makes it effective: The COAST framework separates what the documentation is for (Context), what it should contain (Actions), who reads it (Scenario), and what format to use (Task) — yielding a complete, structured README.
How to adapt these coding prompts
Replace the language, framework, and specific function names with your own. Always specify error handling requirements, code style preferences (type hints, docstrings), and any constraints (no third-party libraries, specific Python version). Paste your actual code where indicated by placeholder text. For generating a custom structured prompt, use the PromptVibe prompt optimizer.
Frequently Asked Questions
Should I trust AI-generated code without review?
No. AI-generated code should always be reviewed by a developer before use in production. AI models can produce plausible-looking but incorrect or insecure code. These are illustrative example prompts — not verified production solutions.
What details make coding prompts more effective?
Specify the programming language, framework version, required output format (type hints, docstrings, error handling), and any constraints (no external libraries, specific coding style). The more precise the task description, the less ambiguous the generated code will be.