Add documentation to code
Generate docstrings and comments in your project's style.
The prompt
Add documentation to this code using [docstring style, e.g. Google style, JSDoc]. ```[language] [paste code] ``` Document each function's purpose, parameters, return value, and errors raised. Add comments only where the logic isn't obvious. Don't change the code itself.
How to use this prompt
- Select Copy prompt.
- Paste it into your AI assistant (ChatGPT, Claude, Gemini, Copilot or similar).
- Replace every part in [square brackets] with your own details before sending.
- Review the answer and ask for changes — the first reply is a draft.
Tip: Check that the documentation matches what the code actually does.
Related prompts
Project README
A clear README for a code project.
Write a README.md for my project. Name: [name] What it does: [description] Tech stack: [list] How to install and run: [steps/commands] Configuration: [environment variables] Licence: [licence] Sections: overview, features, requirements, installation, usage with examples, configuration, contributing, licence. Keep it concise and use code blocks for commands.
How to use it
Run every command in the README yourself to make sure it works.
Write a Python script with explanations
Get working Python code plus an explanation of how it works.
Write a Python script that [describe the task]. Input: [describe input data/format] Output: [describe expected output] Python version: [e.g. 3.12] Libraries allowed: [e.g. standard library only, pandas] Requirements: clear variable names, comments for non-obvious parts, error handling for [likely problems], and a small example of how to run it. After the code, explain how it works in plain English and list any assumptions you made.
How to use it
Run the code on test data first. Read the assumptions list and correct any that are wrong.
Clean a dataset with pandas
Generate pandas code to clean and summarise a messy CSV.
I have a CSV with these columns and sample rows: [paste column names and 5 sample rows — remove personal data] Problems I see: [e.g. missing values, mixed date formats, duplicates] Goal: [e.g. monthly sales totals by region] Write pandas code that loads the file, fixes these problems step by step with comments explaining each choice, and produces the goal output. Print row counts before and after each cleaning step so I can check nothing was lost unexpectedly.
How to use it
Never paste real customer data into an AI tool; use anonymised samples.