How Do You Use GitHub Copilot?

Most people who install GitHub Copilot use about a fifth of it. They get grey text suggesting the rest of a line, press Tab a lot, and form an opinion about whether it is useful based entirely on that. Meanwhile the chat panel, the agent that can edit several files at once, and the inline rewrite tool sit untouched.

That matters because the three parts of Copilot are good at genuinely different things, and the autocomplete is the one with the lowest ceiling. The gap between a developer who only presses Tab and one who uses all three is large enough to look like a difference in tooling rather than habit.

This guide covers what Copilot actually consists of, how to set it up in the editor you already use, how to drive each of its three modes, which plan the features sit behind, and the places it reliably fails.

What is GitHub Copilot, and what do you get?

GitHub Copilot is an AI coding assistant that runs inside your editor and on the GitHub website. It does three fundamentally different jobs, and knowing which one you are reaching for is most of the skill.

Inline suggestions complete code as you type. You write a function signature, Copilot proposes the body in grey text, and Tab accepts it. This also covers suggested next edits, where Copilot predicts the follow-on change elsewhere in the file after you make one edit.

Chat is a conversation in a side panel about your code. You ask why a test fails, request a refactor, or have it explain an unfamiliar block. It can see the files you point it at.

Agent takes a task rather than a question. You describe an outcome, it plans an approach, edits multiple files, runs commands, and presents the result for you to accept or reject. In VS Code you can also ask it to propose a plan before it touches anything.

The first is a typing accelerator. The second is a rubber duck that answers back. The third is closer to delegating a small ticket. Treating all three as autocomplete is the most common way to be disappointed by it.

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How do you set up GitHub Copilot?

Setup is a sign-up plus an extension, and it takes a few minutes.

1. Sign in to a personal GitHub account and get a Copilot plan at github.com/copilot. You can start on Copilot Free without paying, which is enough to decide whether you like it.
2. Install the Copilot extension for your editor. Copilot supports Visual Studio Code, Visual Studio, the JetBrains IDEs, Xcode on macOS, Eclipse, and Windows Terminal, as well as working directly on the GitHub website.
3. Authorise the extension against your GitHub account when it prompts you. This is where the plan gets attached to the editor.
4. Confirm completions are live: open a file, type a function header, and wait a beat for grey text to appear.
5. Open the chat panel so you know where it is before you need it.

In VS Code the chat shortcut is Ctrl+Alt+I on Windows and Linux, or Control+Command+I on a Mac. You can also click the chat icon in the title bar.

The other editors put it in slightly different places. In Visual Studio it is View > GitHub Copilot Chat. In the JetBrains IDEs it is the Copilot Chat icon down the right-hand side of the window. In Xcode it is Editor > GitHub Copilot > Open Chat. On github.com, open a file and click the Copilot icon at the top right to get an Ask Copilot box.

The first-run test worth doing

Before you judge the tool, give it one honest test on real code rather than a toy example.

Open a file in your own codebase that has some established conventions — a service class, a set of API handlers, anything with a visible house style. Write a new function header that follows the same pattern and see what Copilot proposes.

This tells you something a tutorial cannot: how well it has read the surrounding context. Copilot's suggestions are shaped by the file you are in and the files you have open, so a cold editor with one empty file gives a far worse impression than the environment you will really work in.

How do you use each mode well?

Inline suggestions

Type what you want and let it offer. Press Tab to accept, keep typing to ignore.

The lever most people never touch is the comment. A short comment above an empty function is a prompt, and it steers the suggestion far more than the function name alone. "Parse an ISO date, return null on failure" produces markedly better output than a bare parseDate signature.

Accepting a suggestion is a code review, not a keystroke. Read it before you Tab. The failure mode is not that Copilot writes nonsense — it rarely does — but that it writes plausible code that solves a slightly different problem than yours.

Chat

Chat earns its keep on questions about code that already exists. Why does this throw? What does this regex match? What breaks if I change this signature?

Be specific about scope. Point it at the file or selection you mean rather than asking in the abstract, and select the relevant lines in the editor before you ask. "Explain this line" with a line selected is a different and much better question than "explain this file".

Inline chat is the version of this that stays in the editor: describe a focused change where the code is, rather than switching to a panel and back.

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Agent

Agent mode is for tasks with a clear finish line. "Add input validation to these three endpoints and a test for each" is a good agent task. "Make the codebase better" is not.

Three habits make the difference:

• Work on a clean branch. Agent edits multiple files, and you want a trivial way to throw the whole attempt away.
• Ask for the plan first. Having it propose an approach before editing costs one round trip and catches misunderstandings while they are still cheap.
• Review the diff, not the summary. The summary tells you what it believes it did. The diff tells you what it did.

Smart actions cover the small one-off jobs that need no conversation at all, such as generating a commit message or explaining a selection.

Which plan do you need?

Copilot's plans differ on two axes: which models you can reach, and how much agent and chat usage you get. Completions are the cheap part — code completions and next edit suggestions are not metered on paid plans.

• Copilot Free — no cost, limited to roughly 2,000 completions a month and automatic model selection. Fine for evaluating it, tight for daily work.
• Copilot Student — free for verified students, also on automatic model selection.
• Copilot Pro — around $10 a month, a selection of models, and 1,500 monthly AI credits (1,000 base plus 500 flex). The normal choice for an individual developer.
• Copilot Pro+ — around $39 a month, access to premium models, and 7,000 AI credits.
• Copilot Max — around $100 a month, priority access to premium models and 20,000 AI credits.
• Copilot Business — around $19 per seat per month with 1,900 AI credits per user, plus organisation-level policy controls.
• Copilot Enterprise — around $39 per seat per month with 3,900 AI credits per user and priority model access.

GitHub now meters chat and agent work in AI credits rather than the "premium requests" the older documentation described. For organisations and enterprises, usage beyond the pooled allowance is billed per credit.

Treat those figures as a snapshot. Copilot's tiers and billing model have changed more than once, so check the current plans page before you budget against them — the shape of the decision is more stable than the numbers.

The practical guidance: start on Free, move to Pro when the completion cap starts annoying you, and only consider Pro+ or Max if you are leaning on agent mode daily and actually running out of credits.

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Where does GitHub Copilot fall short?

It is confidently wrong about your intent. The code compiles and reads well and solves the adjacent problem. This is the dominant failure and the reason reviewing suggestions is not optional.

It does not know your architecture. Copilot sees open files and the immediate context, not the reasoning behind your module boundaries. It will happily reach across a layer you deliberately kept separate.

Tests are its weakest output. It writes tests that assert the implementation does what the implementation does, which passes forever and catches nothing. Specify the behaviour you want verified, including failure cases.

It lags on fast-moving libraries. For a framework that changed its API recently, suggestions skew toward older patterns. Check against the library's own documentation rather than assuming currency.

Dependency suggestions need checking. Imports that look reasonable are occasionally for packages that do not exist or are not in your project. Verify before installing anything it names.

It cannot tell you what it does not know. Copilot does not flag the boundary of its own context. Ask about behaviour that depends on a config file you have not opened and you get an answer built on an assumption, delivered in the same tone as an answer built on your actual code.

The common thread in all five is that the output is fluent regardless of whether it is right, so fluency is worthless as a quality signal. The only reliable check is reading it against what you were actually trying to do.

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How does it compare to the alternatives?

Copilot's real advantage is not raw model quality, which changes month to month across every tool in this category. It is reach: it is in every mainstream editor, on the GitHub website, and inside the pull request workflow where the rest of your team already works.

Dedicated terminal-first and editor-replacing agents tend to go deeper on large multi-file tasks and cost more. Copilot's bet is breadth and integration — it is already where your code review happens.

For an organisation, the deciding factor is usually governance rather than output. Business and Enterprise plans bring policy controls, seat management and pooled billing, and those are generally what settles the purchase.

If you are weighing Copilot against a dedicated coding agent specifically, our comparison of Claude Code versus GitHub Copilot works through where each one earns its subscription.

Final Thoughts

Using GitHub Copilot well comes down to matching the mode to the job. Inline suggestions for typing you already know how to do, chat for understanding code you did not write, agent for scoped tasks you would otherwise put off.

Set it up on Copilot Free, test it on your own codebase rather than a tutorial example, and find the chat shortcut before you need it. If the completion cap becomes irritating, Pro is the obvious step and the one most individual developers land on.

The one habit that matters more than any setting is reading what it gives you. Copilot's output is almost never garbage and quite often subtly not what you asked for, and those two facts together are what makes unreviewed acceptance expensive.

Used that way it is a genuine accelerator. Used as a Tab key with opinions, it is a slow way to introduce bugs you will find later.