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GitHub Copilot

4 min readbeginnerUpdated 28 Sept 2026
1 · In one line

GitHub Copilot is an AI helper for programmers that offers the next lines of code while you write, explains code when you ask, and can take on jobs you give it.

1 · What it is

GitHub Copilot is a coding helper made by GitHub. GitHub first shared it as a test version, built together with OpenAI. It lives inside code editors such as Xcode, Vim/Neovim, Visual Studio, JetBrains IDEs and Visual Studio Code, and on GitHub.

The best-known part is the inline suggestion. As you type, Copilot guesses what comes next and shows it in faint grey text. Press a key to keep it, or just keep typing. Under the hood, a large language model (a program trained on huge amounts of text and code to predict what comes next) makes that guess from the code around your cursor.

Copilot also sits inside GitHub itself. So one job can move from an issue (a written task), to an agent, to a pull request (a proposed change others can review), and then to a review. There is a free plan with limited features. What you can use depends on your plan, your editor and your organisation’s rules.

2 · Why it exists

Writing software has slow, repetitive parts, and help that guesses can also guess wrong.

Repetitive codeMuch code follows familiar patterns, and Copilot is especially useful for that kind of boilerplate, meaning the same setup code written again and again.
Wrong but plausibleA suggestion can look valid and still be broken or miss what the programmer meant.
Borrowed codeRarely, a suggestion can match code that already exists in public projects.
3 · How it works

Follow one inline suggestion from your keyboard to your file.

How one GitHub Copilot inline suggestion is made You type code or a comment. Copilot takes the code around your cursor as context. A large language model predicts the next code. The prediction is checked against public code, then shown as dimmed ghost text. You accept it, dismiss it or keep typing, which starts the loop again. ONE INLINE SUGGESTION IN YOUR EDITOR You type Context Model predicts Match check Ghost text code, or a comment like // sort by date the code around your cursor an LLM guesses the next line or block matches are blocked or labelled dimmed suggestion at the cursor accept, dismiss or keep typing You review and test anything you accept.
  1. 1 · typeYou write code, or a comment in plain words that describes what you want.
  2. 2 · contextCopilot takes the code you are working on as context.
  3. 3 · predictOne or more large language models turn that context into a suggested next piece of code.
  4. 4 · checkCopilot checks the suggestion against public code and blocks or labels matches, depending on your setting.
  5. 5 · decideThe suggestion appears as dimmed ghost text that you can accept, dismiss or ignore by typing on.

Copilot guesses; you stay responsible for reviewing and testing what you accept.

4 · Where it's used
WhoWhat they askWhat it works with
Student“Write a function that checks whether a word is a palindrome.”A comment describing the function and the open file
Maintainer“Fix this small bug from the backlog and open a pull request.”The issue and the repository
Reviewer“Summarise this pull request and suggest improvements.”The changes in the pull request
5 · What it solves, and what it doesn't
solves
  • It offers code as you type, from single lines to whole blocks.
  • It can turn a plain-language comment into suggested code.
  • Its cloud agent can take an issue (a written bug or task), change code on a separate copy called a branch, and prepare a pull request.
  • It can review pull requests and suggest possible improvements.
doesn't solve
  • It cannot promise correct code, so you still review and test it.
  • It sees nearby code, so it can miss bigger design problems.
  • It is weaker in programming languages that have little public code.
  • Security-sensitive code needs extra care, because a suggestion can open a weakness.
6 · Go deeper
Code generationLLMAI agentsHallucinationPrompt engineering

Sources used

This explainer is written in original language. The links below support its factual claims.

  1. docsAbout GitHub Copilot, GitHub · read 28 Sept 2026
  2. docsGitHub Copilot code suggestions in your IDE, GitHub · read 28 Sept 2026
  3. docsApplication card: GitHub Copilot inline suggestions, GitHub · read 28 Sept 2026
  4. docsAbout GitHub Copilot cloud agent, GitHub · read 28 Sept 2026
  5. docsGitHub Copilot code referencing, GitHub · read 28 Sept 2026
  6. docsPlans for GitHub Copilot, GitHub · read 28 Sept 2026
  7. officialIntroducing GitHub Copilot: your AI pair programmer, GitHub · read 28 Sept 2026
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