GitHub Copilot Reviews & Product Details

Contextual Autocomplete: It suggests entire blocks of code, functions, and tests by analyzing your current file and open tabs.

Boilerplate Reduction: It handles repetitive tasks like writing unit tests, regex, or standard API calls, allowing you to focus on logic.

Natural Language to Code: You can write a comment describing what you want (e.g., // function to validate email using regex), and it will generate the implementation.

Multi-language Support: It works across dozens of languages including Python, JavaScript, TypeScript, Ruby, Go, and Java.

IDE Integration: It lives directly inside popular editors like VS Code, JetBrains, and Neovim, so there is no need to switch windows. Review collected by and hosted on G2.com.

1. Inaccuracy and "Hallucinations"

Code Quality: It often suggests code that is inefficient, outdated, or uses libraries that don't actually exist (hallucinations).

Bugs: It can generate syntactically correct code that contains subtle logical errors, requiring you to spend more time debugging than if you had written it yourself.

2. Context Limitations

Large Projects: It sometimes "forgets" logic established earlier in a file or fails to understand the broader architecture of a complex project.

Proprietary Logic: It struggles with custom frameworks or internal business logic that wasn't part of its public training data.

3. Privacy and Security

Data Training: Many users are concerned about their code being sent to GitHub's servers to train future models. As of early 2026, some users have expressed frustration over "automatic opt-in" policies for data collection.

Vulnerabilities: There is a risk that the AI might suggest patterns that include known security vulnerabilities (like SQL injection) if they were prevalent in its training set. Review collected by and hosted on G2.com.

GitHub copilot is really helpful for speeding up coding and routine tasks. As someone who uses it frequently, I like how it suggests code while I'm typing and helps with small functions, syntax or repetitive parts of the code. The UI feels clean and blends well into tools like VS Code and the integrations with different IDEs make it very convenient to use. It saves time, reduces manual effort and helps maintain a smooth workflow when working on scripts or development tasks. Review collected by and hosted on G2.com.

One slight downside of GitHub copilot is that the suggestions are not always accurate so I still need to review and adjust the code instead of relying on it completely. Review collected by and hosted on G2.com.

Copilot has managed to develop into a fully agentic tool, which is great for agentic coding and development. It’s no longer just an AI assistant, and that completely changes how I can use it day to day. Support for MCP servers, skills, agnets.md, and similar pieces also makes it easier to use alongside other agentic tools.

The UI is fairly intuitive, and I like how it’s directly wired into VS Code. It doesn’t feel like “just an extension” anymore; it feels like a core feature of VS Code now.

The usage limits are also really generous considering the pricing, especially when you compare them to Claude Code, for example. Copilot clearly wins here for me by a lot. Review collected by and hosted on G2.com.

I dislike the data retention policy for Copilot coding agents and Copilot CLI. The retention period is far too long, especially given how much sensitive information is being shared, such as source code. I think they should reconsider this and make changes. It’s not that I don’t trust GitHub, but given the industry I work in, the idea that our data could be stored somewhere for extended periods of time is unacceptable. Review collected by and hosted on G2.com.

GitHub Copilot significantly speeds up development by providing intelligent code suggestions directly in the editor. It’s very effective for reducing repetitive coding tasks, generating boilerplate code, and even suggesting entire functions based on context. The integration with editors like Visual Studio Code is seamless, making it easy to adopt without changing existing workflows.

I also like how it adapts to different programming languages and frameworks. It’s especially useful when working with unfamiliar code or trying out new technologies, as it can suggest patterns and examples quickly. Overall, it improves productivity and helps maintain development flow without constant context switching. Review collected by and hosted on G2.com.

While Copilot is very helpful, the suggestions are not always accurate and still require review. For more complex or critical logic, it’s important to validate the generated code carefully. Sometimes it can suggest outdated or suboptimal patterns. Additionally, over-reliance on suggestions can reduce deeper understanding if not used thoughtfully. Review collected by and hosted on G2.com.

I like Github Copilot because it reduces the time spent on repetitive coding tasks. I also like that it works smoothly inside VS Code, so I dont have to switch between browser tabs and documentation again .Its very easy to implement for beginners.The frequency of use is also high for me because I use it almost daily while coding.It helps with writing functions,fixing small mistakes and speeding up repetitive tasks which makes development smoother.It is ease of integration with tools like VS Code . Review collected by and hosted on G2.com.

One thing I dislike is that sometimes the suggestions look correct but dont fully match the requirement, so you still need to review the code carefully. Another small issue is that getting very specific help through customer support is not always instant , so sometimes you have to rely on documentation. Review collected by and hosted on G2.com.

I use GitHub Copilot regularly during backend development, mainly inside Visual Studio Code. One of the things I like most is how easy it is to start using. After installing the extension and signing in, it immediately begins suggesting code while you type, so there isn’t much setup required.

During my day-to-day work with Python APIs, Copilot helps by suggesting function structures, repetitive code, and common programming patterns. It is especially useful when writing helper functions, validation logic, or basic API endpoints. Instead of searching for syntax or examples online, I often get a useful starting point directly in the editor.

Another advantage is how well it integrates with the existing development workflow. Since I already work in VS Code with Git for version control, Copilot feels like a natural extension of the editor rather than a separate tool. I use it frequently while coding, and it helps maintain development flow and saves time on routine coding tasks. Review collected by and hosted on G2.com.

While Copilot is very helpful, the suggestions are not always perfect, especially for complex or project-specific logic. Sometimes the generated code needs to be adjusted to match the project’s structure or coding style.

Another thing to keep in mind is that developers still need to carefully review the suggested code before using it. I treat Copilot more as a coding assistant that helps speed up development rather than something that can fully replace manual coding. Even with that limitation, it is still very useful for reducing repetitive work. Review collected by and hosted on G2.com.

I use GitHub Copilot to help with coding basic things, formatting my code, and looking for errors. It's integrated into my IDE (VS Code), so I don't have to switch tabs. I appreciate that it comes with multiple LLM options like Claude, ChatGPT, and Gemini, allowing me to switch as I wish. It doesn't run out of credits and has better code handling compared to the traditional web versions. I love that I don't have to copy and paste my code because it already has access and updates it itself. Before changing my code permanently, I can see how the new version performs and the changes it made before deciding to 'keep' it. The integration of multiple AIs helps me check what each has to offer before going with one. The setup was really easy; I just got the extension and connected my GitHub account, and I was good to go. Review collected by and hosted on G2.com.

Sometimes it takes too long to generate a response and keeps meddling with the terminal instead of the actual code, but that happens rarely. Review collected by and hosted on G2.com.

What I like most is how it fits into both my development workflow and our review process. I use it in my IDE to help write code, suggest improvements, and even debug when I’m stuck, which saves a lot of time. We also use it as part of an automated GitHub workflow for code reviews, and it’s helpful in catching basic issues or suggesting changes early. It feels like having an extra pair of eyes, especially for repetitive or boilerplate-heavy tasks. Review collected by and hosted on G2.com.

The suggestions aren’t always accurate, especially for more complex logic or domain-specific code. You still need to review everything carefully, as it can sometimes produce code that looks right but isn’t fully correct. In code reviews, it’s useful but not a replacement for human context, it can miss the bigger picture or intent behind changes. Review collected by and hosted on G2.com.

Copilot fits seamlessly into VS Code with fast, reliable suggestions that keep my flow uninterrupted, even on larger tasks. It saves time on repetitive work, making it worth the cost. Setup is quick, and getting started feels effortless with minimal learning curve.

I also like that it gives access to multiple AI models. Review collected by and hosted on G2.com.

One downside of GitHub Copilot is that it sometimes feels a bit slower as compared to Cursor, especially when working on larger or multi-file changes.

But Copilot is much cheaper (around $10/month vs $20 for Cursor), and for day-to-day coding, it still covers most needs really well. Review collected by and hosted on G2.com.

I like GitHub Copilot because it literally reduces my time on repetitive tasks, like refining my emails and suggesting my coding functions. I like that I can use GitHub Copilot to get an overview of a repository and understand the functionalities, which really helps when I’m looking for main files and functionalities. I love that I can access it inside Visual Studio Code. It immediately starts suggesting code and improving it for me. GitHub Copilot is especially useful in writing helper functions, validations, and logic. It’s great that I don't have to switch between tabs when I'm working because I can access it easily both from GitHub and Visual Studio Code. I appreciate the different models provided by Copilot as they really help a lot. I find the customer support and the community very helpful, and I feel like the platform is well-supported, which gives me trust when relying on it for development. I think GitHub Copilot is flexible and can be used by anyone, not just developers—it can help with sales data analysis or marketing strategies. It also helps me with documentation by providing outputs in a structured way. The initial setup was smooth and very straightforward, making it user-friendly and beginner-friendly. Review collected by and hosted on G2.com.

Sometimes the suggestions are not very up to date, especially with recent changes in API versions like Azure's. GitHub Copilot doesn't always have knowledge of the latest API updates, which can be problematic when working with new features or changes. Additionally, it requires a stable internet connection, which is a limiting factor. Review collected by and hosted on G2.com.