Let’s be honest, AI tools are popping up everywhere in our work lives. For software developers, this change has been huge, and no tool has caused more of a stir than GitHub Copilot. But with all the buzz, it’s hard to get a straight answer on what it actually does and, more importantly, if it’s any good.
This guide is here to give you the real story. We’ll walk through a clear-eyed look at GitHub Copilot, covering what it is, its main features, where it really helps, and its limitations (because yes, there are some). We’ll also see how it fits into the bigger picture of AI assistants that are changing how whole companies operate.
So what is GitHub Copilot, really?
The simplest way to think of GitHub Copilot is as an "AI pair programmer" that lives right in your code editor. It’s much more than the basic autocompletion you’re probably used to. It’s powered by some serious AI models from GitHub, OpenAI, and Microsoft, and it has been trained on billions of lines of code from public repositories. This gives it a ridiculously deep understanding of different programming languages, frameworks, and coding styles.
Here’s how it works in practice: GitHub Copilot looks at the code you’re writing, but it doesn’t just see the single line you’re on. It takes in the whole file, other files you have open, and the general structure of your project. Using all that context, it suggests everything from a single line to entire functions. It plugs directly into popular IDEs like Visual Studio Code, Visual Studio, and the JetBrains family, so it feels like a natural extension of your workflow.
The core features of GitHub Copilot explained
To really get what GitHub Copilot can do, it helps to look at its main functions one by one. It’s definitely not a one-trick pony.
GitHub Copilot code completions and suggestions
This is the classic feature that got everyone talking. At its heart, GitHub Copilot is an autocomplete on steroids that understands not just syntax, but what you’re trying to achieve with your code. As you type, it offers "ghost text" suggestions that you can accept with a tap of the Tab key. This saves a ton of time on boilerplate code, common patterns, or just finishing a thought. It’s all about cutting down the repetitive stuff so you can focus on solving the actual problems.
Conversational coding with GitHub Copilot Chat
These days, you can talk to almost any AI, and GitHub Copilot is no different. It has a chat interface built right in, letting you ask questions in plain English. You can highlight a confusing block of code and ask, "What on earth does this do?" or "Can you refactor this to be more efficient?" You can even ask it to write documentation, whip up some unit tests for a function, or brainstorm a few different ways to tackle a problem. It turns your IDE into a place where you can collaborate with your AI assistant.
Autonomous tasks with the new GitHub Copilot coding agent
The newest version of GitHub Copilot includes a coding agent, which is a whole other level of assistance. Instead of just suggesting code snippets, you can now give it entire tasks to work on. Straight from the GitHub website, you can take an issue, like a bug report, and assign it to the Copilot agent. The agent then goes to work by itself. It reads the codebase, figures out a plan, writes the code, and even submits a pull request for a human teammate to look over. This is a massive help for automating tasks that have multiple steps, freeing up developers to think about bigger architectural questions instead of small fixes.
Practical use cases: Where GitHub Copilot shines (and where it doesn’t)
Knowing the features is one thing, but knowing when to use them is what really makes a difference. Here’s a realistic look at where GitHub Copilot is a lifesaver and where you need to be a bit more cautious.
Speeding up development and cutting out boilerplate with GitHub Copilot
This is GitHub Copilot’s sweet spot. It’s fantastic at accelerating common, repetitive coding jobs. Just think about all the time you sink into writing code that isn’t central to what your app does:
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Generating unit tests for your functions.
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Creating dummy data or mock objects for your test environments.
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Scaffolding out a new API endpoint in your web framework.
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Building data models or classes based on a schema.
For this kind of work, it’s an incredible productivity boost. It takes care of the boring parts so you can jump into the more interesting challenges.
Learning new languages and frameworks with GitHub Copilot
If you’re a seasoned developer trying to pick up a new language, GitHub Copilot can be a great learning partner. Instead of constantly switching over to your browser to look up syntax, you can see idiomatic code suggestions right in your editor. It helps you learn by doing, showing you the common patterns and the typical way to structure things in a language you’re not used to. It won’t teach you the fundamentals from scratch, but it can definitely shorten the time it takes to get up and running.
This comprehensive guide walks through the basics of using GitHub Copilot, from simple completions to more advanced features.The limitations of GitHub Copilot: It’s not a mind reader
As powerful as it is, GitHub Copilot isn’t magic, and trusting it blindly is a bad idea. It has some key limitations that you really need to keep in mind.
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It doesn’t know your business: While it’s great with code context, it often has no idea about the broader business logic of your application. It might suggest code that is technically perfect but functionally wrong because it doesn’t know that, say, "premium" users are supposed to have different access rights than "free" users.
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The code isn’t always perfect: It can sometimes produce code that is inefficient, buggy, or violates basic software principles like DRY (Don’t Repeat Yourself). The code it suggests looks right, but it hasn’t been tested or reviewed by a human. A solid code review process is still absolutely essential.
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It can be a crutch for learning: For junior developers, relying too heavily on GitHub Copilot can be a problem. It risks preventing them from developing the core problem-solving skills that make a great engineer. If you never struggle through writing an algorithm yourself, you might not ever truly get it.
| Strengths | Weaknesses | | :------------------------------------– | :------------------------------------------------ | | Writing boilerplate code and tests | Understanding complex business logic | | Accelerating repetitive tasks | Potential to introduce subtle bugs | | Learning new language syntax | Can violate code quality principles (DRY) | | Prototyping and scaffolding quickly | Can be a crutch for inexperienced developers |
The bigger picture: AI assistants are changing work everywhere
If you zoom out a bit, GitHub Copilot is a perfect example of a bigger trend: specialized AI assistants are popping up in every profession. It’s not just developers. Every team, from customer support to sales, is seeing a similar change as AI tools built for their specific jobs arrive on the scene.
Just like GitHub Copilot is an AI pair programmer for developers, tools like eesel AI act as an AI support agent for customer-facing teams. The idea is the same, using AI to help humans, but the execution is totally different. Instead of learning from public code, an AI for support like eesel AI is trained securely on your company’s private knowledge, like past support tickets, help articles, and internal docs from places like Confluence or Google Docs. This lets it give accurate, specific answers that a general AI just couldn’t provide. It’s all about using the right tool, with the right data, for the right job.
Choosing the right AI assistant for your team
Figuring out the different ways AI can be applied is the key to investing wisely. A tool that’s brilliant for one team might be completely useless for another.
When to use GitHub Copilot
The use case for GitHub Copilot is crystal clear: it’s for software developers and engineering teams. It lives in the IDE and is built to speed up the hands-on work of writing, refactoring, documenting, and testing code. If your job involves writing code, it’s a tool you should probably be looking at.
When you need an AI assistant for support and internal knowledge
For teams outside of engineering, the challenges are different. The problem isn’t writing code; it’s dealing with a high volume of repetitive questions, either from customers or from other employees. Answering the same things over and over is a major drag on productivity and a fast track to burnout.
This is where a dedicated AI support assistant makes a huge difference. By connecting to your helpdesk, like Zendesk, Freshdesk, or Intercom, an AI platform can provide instant answers to common questions, freeing up your human team for the trickier issues. This is where eesel AI really shines:
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You can get it running in minutes: Unlike complex developer tools that can take a while to set up, platforms like eesel AI are self-serve. You can connect your knowledge sources and have a working AI assistant live in minutes, without needing to pull engineers off their projects.
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You’re still in control: Developers have to review every line of AI-generated code, and support teams need similar control over automation. eesel AI’s workflow engine lets you decide exactly which tickets the AI should handle and what it’s allowed to do, whether that’s escalating to a human or looking up live order info. You get the benefits of automation without losing control over the customer experience.
GitHub Copilot and a new way of working
GitHub Copilot is a tool that’s genuinely changing the day-to-day work of a software developer. It streamlines tedious tasks and acts as a powerful sidekick for both building new things and learning on the fly.
But the bigger story here is that this is just one piece of a much larger puzzle. Specialized AI assistants are becoming essential across every part of a business. The secret to success isn’t just "using AI," but choosing tools that were built for specific jobs. That means GitHub Copilot for writing code, and platforms like eesel AI for automating support and managing internal knowledge.
Get started with AI-powered support today
Ready to bring the power of AI to your customer support team? eesel AI learns from your company’s knowledge to automate resolutions and help out your agents. The setup is self-serve and only takes a few minutes.
Start your free trial today or book a demo to see it in action.
Frequently asked questions
It’s a useful tool but should be used with caution by learners. While it can accelerate learning new syntax and patterns, relying on it too heavily can prevent junior developers from building the core problem-solving skills they get from struggling through problems themselves.
You should treat its suggestions as a first draft, not a final product. The code it produces can sometimes be inefficient or contain subtle bugs, so a thorough human review process is still absolutely essential before any code is committed.
It learns from the context of your open files and project structure. While it doesn’t have explicit knowledge of your internal style guide, it will try to match the patterns and style of the code it sees in your project, making its suggestions feel more natural.
You can use the chat feature to ask it to perform larger tasks like refactoring an entire function for better performance or generating a full suite of unit tests. The new agent can even take on a multi-step task from a GitHub issue and create a pull request on its own.
GitHub Copilot processes your code snippets to provide suggestions, but it’s designed with privacy in mind for business users. Your code is transmitted securely and is not used to train the public models for other users, ensuring your proprietary code remains confidential.
