GitHub Copilot and ChatGPT are two generative AI tools that help coders develop applications.
Developed by GitHub and OpenAI, Copilot is specifically focused on code completion, providing suggestions for lines of code or entire functions directly within your integrated development environment (IDE). Built on OpenAI’s language model.
Developed by OpenAI and released in November 2022, ChatGPT is accessible as a separate tool, suitable for summarizing complex code and generating starting templates for specific coding tasks.
Additionally, ChatGPT is designed for common language tasks, so it can answer a wide range of questions outside of common programming workflows. GitHub Copilot goes head-on with code completion.
“The main difference is [between the two] The tight integration between Copilot and Visual Studio Code and the fact that Copilot is trained with a huge amount of code from GitHub and elsewhere,” explained Guido Hoffmann, Technical Fellow at Tech Soft 3D. I’m here.
Copilot users can get help directly within popular tools such as Visual Studio, VS Code, Neovim and JetBrains IDEs. This also allows you to analyze the larger context of your code without cutting short snippets and pasting them into his ChatGPT.
Both tools promise to make developers more productive by automating the writing of mundane boilerplate code. GitHub recently announced Copilot X as a preview of its future vision. This vision proposes plans to combine some of the best features of Copilot and GPT.
Both ChatGPT and Copilot are free to try. A ChatGPT subscription that gives you access to the latest language models is $20/month. Copilot pricing starts at $10/month for individuals and $19/month for businesses.
GitHub Copilot vs. ChatGPT: How it works
While the user experience and workflows of ChatGPT and GitHub Copilot are different, there are some similarities regarding the underlying technology. Both systems utilize large-scale language models (LLMs) to generate responses and suggestions.
While ChatGPT’s LLM is trained on human language data, GitHub Copilot’s LLM, called Codex, is fine-tuned on vast data sets of source code and natural language text, says JetBrains data analytics and machine learning. (ML) Team Leader Nikita Povarov explains: .
“This fine-tuning process allows Codex to understand the syntax and structure of the code,” says Povarov.
Below is the response from OpenAI’s Codex to the author’s request.
ChatGPT, by contrast, is a general-purpose conversational AI platform that uses natural language processing to respond to user input. Suitable for more extensive and complex tasks.
Agur Jõgi, CTO of CRM platform Pipedrive, said the most common application development use case for ChatGPT is understanding and writing code based on task descriptions. Below is his ChatGPT example illustrating the code submitted by the author.
“Overall, Copilot is a great tool for quick, tactical tasks, and ChatGPT is good for a wider range of tasks. will be more advanced,” Jõgi said.
Comparing the pros and cons of GitHub Copilot and ChatGPT
Here’s an overview of the pros and cons of each of the tools.
GitHub Copilot
strength. GitHub Copilot excels at generating code snippets and suggestions based on the context of the code being written, said co-founder and CEO of MindsDB, a platform designed to democratize ML. says Jorge Torres.
Copilot can suggest code lines, variables, and function names that are relevant to the context of your code and help you with detailed snippets. GitHub Copilot also acts as a code completion agent that can perform tasks that the regular code completion tools built into the IDE can’t do.
So it saves developers time and helps them code more efficiently. Additionally, GitHub Copilot can learn from code written by developers, improving its suggestions and accuracy over time.
Another strength of GitHub Copilot for coding is its integration into IDEs, which makes real-time coding more efficient than ChatGPT and proves an overall better user experience for programmers, Jogi said. . “It’s built into his GitHub ecosystem, so developers can use it without context switching or opening additional tools,” he said.
weakness. GitHub Copilot’s weaknesses include generating incorrect or inefficient code suggestions, Torres said. Additionally, it may not be suitable for complex programming tasks that require extensive knowledge and expertise.
Chat GPT
strengths. ChatGPT’s strength is its ability to automate customer service interactions and provide virtual assistance for a variety of tasks, Torres said. It can also generate text-based content such as articles, stories, summaries, etc. to help with content creation.
Povarov also found that ChatGPT produced a wide range of code responses and helped explain code concepts. It is also suitable for non-technical stakeholders and is more flexible. Users communicate through a chat-like interface with a helper who can answer almost any question. This flexibility allows users to clarify and reformulate questions to derive more sophisticated answers and distinctions.
Jõgi also uses ChatGPT to generate functional and unit tests and validate the results. This streamlines your test-driven development practices.
weakness. As for the drawbacks, Torres found that ChatGPT can struggle with complex or technical language and doesn’t always produce accurate or appropriate responses. Additionally, it may not be suitable for applications that require real-time interaction, such as games and trading.
Similarities between Copilot and ChatGPT
Both tools leverage OpenAI’s GPT LLM and produce results in different ways. It also suggests different ways to improve all kinds of tasks, not just programming. For example, Microsoft has rolled out a series of Copilots for various Office applications and integrated ChatGPT functionality into the Bing search engine.
“Both represent the future of generative AI experience design, helping AI-powered assistants accomplish tasks more efficiently and effectively,” said Torres. As AI technology continues to improve, we will see increasingly sophisticated AI-driven assistants capable of understanding human language and context more accurately and producing more complex and sophisticated output.
Both tools demonstrate how generative AI can improve developer productivity by automating routine tasks. Both types of functionality may eventually be integrated directly into development tools.
“In the future, AI assistants similar to ChatGPT will be integrated directly into IDEs, allowing developers to communicate with AI assistants to checkout code from repositories, run tests, and build code. You may be able to ask to perform routine tasks such as,” Povalov said.
If something goes wrong, like a test failing or a build crashing, the developer can ask the assistant to identify the error and suggest ways to fix it. Assistants can do this, reducing the time and effort required to maintain your codebase.
Future Preview: Co-Pilot X’s Wings
GitHub recently announced Copilot X, a technical preview of its vision for the future of AI-assisted software development. New tools combine chat and terminal interfaces directly into the IDE. It promises to automate more aspects of your development experience. For example, it can detect code changes and automatically suggest explanations. pull requestwith software updates.
This new offering promises context-aware conversations for explaining code, finding bugs, and suggesting fixes. We also personalize answers that link to official documentation. We also commit to writing software tests and identifying any missing tests that may be required.
GitHub is X A placeholder for new services such as security, pull requests, documentation, and testing.of X It also suggests the magnitude of the impact on developer productivity.
At this time, Copilot X represents GitHub’s vision for the future, but is not available as a product. The company said it wants to take the time to determine how best to bring these new features to customers.
