GitHub Copilot Prompts: Copy and Paste Ideas for Coding Jobs

I'm a marketer, not a full-time developer, but plenty of the small business owners and creators I work with now look after their own website code, scripts and little apps. GitHub Copilot Chat is often the tool they reach for, and the difference between a vague question and a clear one shows up straight away in what comes back.

This library is for GitHub Copilot inside your code editor, not Microsoft's general Copilot assistant. Each prompt is grouped by the job in front of you. Copy it into the Chat view or inline chat, swap the [brackets] for your own file names and details, and highlight the code you mean first. Two rules before you start: review and test anything Copilot writes before you run it, and never paste passwords, tokens or API keys into a prompt.

GitHub Copilot prompts to explain code you didn't write

Inherited a theme, a plugin or a colleague's script? Start by understanding it. Select the code first so Copilot knows exactly what you're asking about.

Plain-English walkthrough

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Explain the selected function in plain English for someone new to [language]. Describe what goes in, what comes out, and any side effects, step by step.

Map a whole file

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Give me a short map of [file name]: the main functions, how they call each other, and which parts I'd need to change to adjust [behaviour].

Decode a confusing line

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This line in [file name] confuses me: [paste line]. Break it into smaller pieces and tell me what each piece does and why someone might write it this way.

Find the hidden assumptions

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List the assumptions the selected code makes about its inputs, such as types, empty values or ordering, and point out which ones could break with real data.

GitHub Copilot prompts for writing tests

GitHub's guidance says examples help Copilot understand what you want, and tests are one of the clearest examples you can give.

Starter unit tests

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Write unit tests for [function name] using [test framework]. Cover the normal case, an empty input and one invalid input. Keep each test small and give it a clear name.

Edge cases I've missed

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Look at the selected function and list edge cases my current tests in [test file] don't cover. Then write tests for the three most likely to cause a bug.

Test from a bug report

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A user reported this: [describe the bug]. Write a failing test that reproduces it in [test file] before we change any code.

Readable test data

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Create a small set of realistic but fake sample data for testing [feature], with no real names, emails or account details, and show how to load it in [test framework].

GitHub Copilot prompts to refactor and tidy code

Refactoring goes better in small, checked steps. Ask for one change at a time and run your tests between each.

Split a long function

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The selected function does too much. Suggest how to split it into smaller functions with clear names, keeping the behaviour exactly the same. Show the result one step at a time.

Remove repetition

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Find repeated logic across [file name] and [file name] and propose one shared helper. Explain what changes in each file before writing the code.

Make it easier to read

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Rewrite the selected code so it's easier to read: clearer variable names, fewer nested conditions and early returns where it helps. Don't change what it does.

Modernise old syntax

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This code uses older [language] patterns. Update it to current conventions for [language version or framework] and list each change so I can review it.

GitHub Copilot prompts for documentation and comments

Good notes save you hours when you come back to a project months later. These prompts turn working code into something a stranger could follow.

Docstrings for a module

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Add clear docstrings to every public function in [file name], describing parameters, return values and errors raised, in the [style, for example Google or JSDoc] format.

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README for a small project

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Draft a README for this project with a one-line summary, how to install it, how to run it locally and how to run the tests. Use the files open in my editor as the source.

Explain why, not what

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Add short comments to the selected code that explain why each tricky decision was made, not what each line does. Skip comments where the code is already obvious.

Commit message from my changes

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Write a short commit message for the changes I've made to [file name]: a summary line under a sentence long, then two or three bullet points on what changed and why.

GitHub Copilot prompts for debugging errors

Give Copilot the error message and the code around it. GitHub says Copilot Chat can suggest fixes based on both.

Read the error for me

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I'm getting this error: [paste error message]. Using the selected code, explain what's most likely causing it and suggest the smallest fix to try first.

Why does this give the wrong result?

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The selected function returns [wrong result] when I pass [input], but I expected [expected result]. Walk through the logic with that input and find where it goes wrong.

Add logging to narrow it down

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Suggest where to add temporary logging in [file name] to track down why [problem happens]. Keep the logs short and easy to remove afterwards.

Works locally, fails elsewhere

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This code runs on my machine but fails on [server or environment] with [error]. List likely differences, such as versions, paths or settings, and how to check each one.

GitHub Copilot prompts for code reviews before you merge

A second pair of eyes helps, even an automated one. Use these alongside a human review, not instead of it.

Review my change

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Review the selected changes for bugs, unclear names and missing error handling. Group your comments by severity and explain each one briefly.

Security check

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Check the selected code for common security problems, such as unsanitised input, hard-coded passwords or injection risks, and explain how to fix any you find.

Pull request summary

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Summarise the changes in this pull request for a non-technical teammate: what problem it solves, what changes for users and anything they should test.

How to get better results from GitHub Copilot prompts

GitHub publishes its own prompt engineering guide for Copilot Chat. These are the points from it that matter most day to day.

• Start broad, then get specific. GitHub suggests describing the overall goal first, then listing your requirements one by one.
• Show, don't just tell. Sample inputs, expected outputs or an existing unit test help Copilot understand what you're after.
• Break big jobs into small ones. Ask for one function or one change at a time rather than a whole feature in a single prompt.
• Avoid vague words such as "this" or "it". Name the function or file, and open or highlight the code you mean, because Copilot uses your open files as context.
• Start a new thread for a new task so old questions don't muddle the answer, and if a reply misses the mark, rephrase and try again.
• Use the shortcuts. In VS Code, typing @ brings in a chat participant, # adds context such as a file, and slash commands like /explain cover common requests.

Above all, check the output. GitHub's own responsible use page says to always review and test generated code thoroughly, and warns that Copilot Chat can produce code that looks valid but isn't correct. Keep secrets out of your prompts and your code, too: GitHub warns against hard-coded passwords.

If you're choosing between assistants, my rundown of the best vibe coding tools compares Copilot with the alternatives. And if you're still deciding what to learn first, this look at the top programming languages to learn is a good place to start.

If you want the full set, start at the ChatGPT Prompts and Limits: 30 Guides to Fun Prompts, Photo Edits and Daily Caps. To write your own, try the free tool that writes the prompt for you.

Frequently asked questions

Where do I type GitHub Copilot prompts?

In your editor's Copilot Chat. In VS Code you can use the Chat view or start inline chat right in the editor, which is handy when you've highlighted the code you want help with.

Is GitHub Copilot the same as Microsoft Copilot?

No. GitHub Copilot is a coding assistant that works inside your code editor and on GitHub. Microsoft Copilot is a general assistant for everyday questions, writing and office work.

Can I trust the code Copilot writes?

Treat it as a draft. GitHub says Copilot Chat may produce code that looks valid but isn't correct, and asks users to review and test generated code thoroughly before relying on it.

Is it safe to paste API keys or passwords into Copilot Chat?

Don't do it. Replace secrets with placeholders such as [API_KEY] before you paste code, and keep them in environment variables rather than in the code itself.

Why does Copilot ignore part of my request?

The prompt is usually trying to do too much. GitHub's guide suggests breaking complex tasks into small steps, naming the exact code you mean and starting a new thread for a new task.