March 13, 2026 14 min to read

Getting Started with GitHub Copilot in VS Code

A beginner-friendly guide to setting up Copilot in VS Code and using it well from day one

Image credit: GitHub Blog

If you’re getting started with GitHub Copilot, treat it as a practical coding assistant instead of a magic code generator. In VS Code, it can suggest code as you type, explain files, draft tests, and help you fix small bugs without leaving the editor. This guide focuses on the beginner path: get Copilot running, try a few safe prompts, and build habits that keep you productive without trusting every suggestion blindly.

This walkthrough uses VS Code because it has the simplest first-run experience. However, the same core ideas carry to other supported IDEs and to GitHub on the web. If your real goal is terminal-first automation, take that as a separate path and read Introduction to Copilot CLI after you understand the editor workflow.

TL;DR: GitHub’s research with more than 2,000 developers found 73% said Copilot helped them stay in flow. The best beginner path is to set it up in VS Code, try inline suggestions and chat on a small file, and review every generated result before keeping it.

We analyzed GitHub Docs and VS Code guidance to keep this walkthrough focused on the smallest workflow that helps on day one.

What Does Getting Started with GitHub Copilot Actually Look Like?

GitHub’s published research with more than 2,000 developers found that Copilot’s value is not only speed: 73% said it helped them stay in flow and 87% said it preserved mental effort on repetitive tasks (GitHub Research, 2024 update). Importantly, for beginners, that matters more than any giant feature list.

GitHub Copilot is an AI coding assistant that uses your prompt and nearby code to suggest completions, explanations, and edits. Inline suggestions are ghost-text completions that appear as you type. Copilot Chat is a chat interface in VS Code that can explain code, draft tests, and suggest fixes.

The simplest way to understand Copilot is to split it into a few surfaces you can use right away:

Copilot is not a replacement for programming knowledge. It is closer to a fast assistant that reacts to your current file, your prompt, and the surrounding project context. For example, if you ask for code with no context, you often get generic output. If you ask for help on a real file with clear constraints, the answers become much more useful.

In other words, one practical way to avoid disappointment is to learn Copilot in layers. Start with inline suggestions for tiny wins, then use chat for explanation and review, and only later try more autonomous workflows. Beginners who skip straight to the biggest features often blame Copilot for mistakes that are really scope mistakes.

What Do You Need Before Setting Up GitHub Copilot?

GitHub Docs currently list Copilot Free with 2,000 completions and 50 chat messages per month, which is enough for a first-day setup session and some real practice in VS Code (GitHub Docs, 2026). That makes the entry barrier much lower than many beginners expect.

You only need a few things:

• A GitHub account
• Access to GitHub Copilot through Copilot Free, a paid plan, or an organization seat
• A current version of VS Code
• A small practice project or scratch folder where you can safely experiment

You do not need a big codebase to start. In fact, a small JavaScript, Python, or TypeScript file is a better place to begin because you can clearly see what Copilot changes and decide if the suggestion helps.

However, if you are unsure whether you need to pay, start with the free option. The point of day one is not to compare every plan. The point is to learn whether inline suggestions and chat fit the way you already work.

How Do You Set Up GitHub Copilot in VS Code?

Because Copilot Free includes 2,000 completions and 50 chat messages per month, a brand-new user can finish setup and still have enough room to test both suggestions and chat immediately (GitHub Docs, 2026). In VS Code, the setup path is short and does not require manual extension hunting in the typical first-run flow.

Use this sequence:

1. Open VS Code.
2. Hover over the Copilot icon in the status bar.
3. Select Use AI Features.
4. Sign in with your GitHub account.
5. Complete the browser sign-in flow.
6. Return to VS Code and wait for Copilot to become active.

Meanwhile, once you are signed in, VS Code will use your existing Copilot entitlement. If you do not already have one, VS Code can guide you onto Copilot Free so you can start testing the feature right away (VS Code Docs, 2026).

Additionally, here is how to verify that setup worked:

• The Copilot icon in the status bar should look active.
• The chat icon should be available in the title bar or command center.
• Inline suggestions should appear when you start writing code in a supported file.

That said, if your subscription is not detected, the most common cause is the wrong GitHub account. In VS Code, open the Accounts menu, sign out of the current GitHub account, and sign in again with the account that actually has Copilot access (VS Code FAQ, 2026).

How Should You Use Your First Inline Suggestion and Chat Prompt?

In GitHub’s controlled study with 95 professional developers, the group using Copilot finished a coding task 55% faster on average and completed it more often, 78% versus 70%, than the group without Copilot (GitHub Research, 2024 update). The easiest way to feel that benefit is to start with a tiny, low-risk task.

For example, create a new file and start with a simple function header:

function formatDisplayName(firstName, lastName) {

Copilot will usually suggest the rest of the function as ghost text. Press Tab to accept it. If you do not like the first version, use Alt+] and Alt+[ in Windows or Linux to cycle through alternatives.

Alternatively, you can guide Copilot with a natural-language comment:

// create a function that returns true when an email address looks valid
function isValidEmail(email) {

After that, open Copilot Chat and ask something concrete about the code you now have:

Explain this function in simple terms and list two edge cases I should test.

Then try a test-focused prompt:

/tests using Jest and include cases for empty input, invalid input, and a normal valid case

In addition, if you want to make a focused edit without leaving the file, select a block of code and press Ctrl+I to open inline chat in the editor. That is a good way to ask for one change at a time, such as input validation or a clearer variable name.

What Prompt Patterns Work Better for Beginners?

GitHub’s research found 87% of users said Copilot helped preserve mental effort on repetitive tasks (GitHub Research, 2024 update). However, the practical catch is that Copilot becomes much more useful when your prompt removes ambiguity instead of sounding clever or broad.

From our analysis, beginners get better answers when they ask for one change at a time on a real file instead of asking Copilot to solve everything in one turn.

The strongest beginner prompt pattern is simple:

1. State the goal.
2. Add the relevant context.
3. Add constraints.
4. Give an example if needed.

Weak prompt:

Help me fix this.

For instance, a better prompt is:

Explain why this function fails on empty input, then refactor it so it returns early when the value is missing. Keep the current function name and write the example in plain JavaScript.

Better still when you have project context:

Use the style of this file. Add validation similar to the submit handler above, and do not introduce a new dependency.

GitHub’s official prompting guidance is consistent here: start general, then get specific, give examples, and break complex work into smaller asks (GitHub Docs, 2026).

That said, many beginners decide Copilot is weak after one vague prompt. In practice, the better test is whether Copilot improves after you add scope, constraints, and an example. If the answer gets sharper each turn, the model is usually fine and the prompt needed work.

Similarly, if you want repository-level behavior later, such as shared instructions for your whole project, the next logical step is The .github Folder for GitHub Copilot. For day one, keep the habit smaller: ask one thing, verify it, then ask the next thing.

How Can You Use GitHub Copilot Safely?

If you enable blocking for suggestions matching public code, GitHub says Copilot compares suggestions with roughly 150 characters of surrounding code against public repositories and hides near matches when block mode is supported (GitHub Docs, 2026). However, that does not replace review, testing, or security judgment.

The safest beginner habits are boring on purpose:

• Review every suggestion before accepting it.
• Run tests or at least execute the code path you changed.
• Never paste secrets, tokens, or production credentials into chat.
• In particular, be extra careful with auth, payments, file deletion, and security-sensitive code.
• Prefer small edits you can verify over large rewrites you cannot reason about.

Meanwhile, there are also a few settings worth knowing on day one in your Copilot settings on GitHub:

• Suggestions matching public code: allow or block
• Prompt and suggestion collection: choose whether GitHub can use that data for product improvement
• Web search for Copilot Chat: enable or disable Bing-backed web search

Additionally, GitHub also states that prompts, suggestions, and code snippets are not used for AI model training by default in personal Copilot settings (GitHub Docs, 2026). We found in GitHub’s responsible-use guidance that the safest beginner habit is still to review every accepted suggestion before you keep it. However, that context does not change the need for human review.

In other words, the safest mental model is this: Copilot is very good at producing a plausible first draft. Your job is to decide whether that draft fits your codebase, your security bar, and the actual requirement. If you keep that relationship clear, Copilot becomes much easier to trust in the right places.

What Should You Do When GitHub Copilot Does Not Work?

GitHub Docs note that content exclusion changes can take up to 30 minutes to reach IDEs that are already open, which means a broken Copilot session can sometimes be a policy or sync delay rather than a bad install (GitHub Docs, 2026). Therefore, start with the boring checks before you assume the service is down.

Use this checklist first:

Similarly, if the quick checks fail, use the built-in diagnostics:

1. Open View > Output and choose GitHub Copilot from the dropdown.
2. Run Developer: Open Extension Logs Folder from the command palette.
3. Run Developer: Chat Diagnostics to inspect reachability and network issues.

Those three views usually tell you whether the problem is sign-in, extension state, or network connectivity (GitHub Docs, 2026).

Frequently Asked Questions

Is Copilot Free enough for beginners?

Yes. GitHub Docs currently list Copilot Free with 2,000 completions and 50 chat messages per month, which is enough for a beginner to set up Copilot, test inline suggestions, ask a few chat questions, and decide whether heavier usage is worth paying for (GitHub Docs, 2026).

Should I start with inline suggestions or chat?

Start with inline suggestions for tiny edits, then use chat when you want explanation or structured help. GitHub’s research found 73% of users felt more in flow with Copilot, and inline suggestions are the easiest way to feel that low-friction benefit first (GitHub Research, 2024 update).

Can Copilot write tests for me?

Yes. GitHub Docs explicitly describe test generation as a supported Copilot Chat use case, and slash commands like /tests make it easier to ask for that draft quickly. Still, use the output as a first pass only; GitHub’s 95-developer study measured faster task completion, not guaranteed test quality (GitHub Docs, 2026).

Can I trust every answer if it looks correct?

No. GitHub’s responsible-use guidance says outputs can be inaccurate, insecure, biased, or occasionally similar to public code. Even with public-code blocking, matching checks use roughly 150 characters of surrounding code, which is a useful filter, not a correctness guarantee (GitHub Docs, 2026).

Where To Go Next

In other words, getting started with GitHub Copilot is less about learning every feature and more about building a clean first loop: sign in, accept one useful suggestion, ask one good chat question, and verify what changed. Once that loop feels normal, the rest of the platform makes much more sense.

If you want more background on the author and site, see About. If you want to suggest a follow-up guide or ask a question, use Contact.

If you want the next step after this beginner guide, these are the most relevant follow-ups:

• The .github Folder for GitHub Copilot for repository instructions, prompts, agents, skills, hooks, and MCP.
• Introduction to Copilot CLI for terminal-first workflows.
• Copilot CLI vs VS Code Copilot Chat for choosing the right tool in the right context.