What Is Github Copilot?
GitHub Copilot is an AI-powered assistant that helps you write code faster. It is a product of a collaboration between GitHub and OpenAI. This tool uses a large language model (LLM), GPT-4 at the time of this writing, to suggest lines or blocks of code as you type. It is designed to understand the context of your code and provide suggestions that fit your coding style and project structure.
GitHub Copilot works directly in your editor, supporting several programming languages and frameworks. It was trained on a large corpus of publicly available code, making it applicable to a wide array of tasks, and suitable both for beginners and experienced coders.
However, while GitHub Copilot can provide code suggestions, i t may not always provide the most efficient or optimal solution, and so it's essential to review its outputs. The quality of the suggestions can greatly vary depending on the complexity of the task and the language used.
What Is ChatGPT?
ChatGPT is a chatbot developed by OpenAI, based on the GPT series of large language models. It was initially powered by GPT 3.5 and now also offers GPT 4, the company’s most capable model, as part of the paid Plus subscription. ChatGPT generates human-like text based on natural language ‘prompts’. It has been trained on a diverse range of internet text, including both text and code, enabling it to handle a variety of topics and conversations.
ChatGPT has numerous applications, including content creation, tutoring, and coding assistance. When used for coding assistance, it can help explain complex code, suggest improvements, and provide answers to coding problems. It can also generate code snippets in response to specific queries.
However, unlike GitHub Copilot, ChatGPT was not specifically trained or tuned on code datasets. Therefore, it might not provide the same quality of responses to code-specific requests. It also cannot operate within a developer’s IDE. On the other hand, ChatGPT can be used for additional tasks that are outside the scope of Github Copilot, such as writing full tutorials with code.
This is part of a series of articles about AI tools for developers
GitHub Copilot vs. ChatGPT: Key Differences
1. Use Cases and Applicability
GitHub Copilot and ChatGPT serve different, though somewhat overlapping, purposes. GitHub Copilot is primarily designed for software development, providing code suggestions and completions in various programming languages. It helps programmers write code more efficiently by suggesting entire lines or blocks of code, thereby streamlining the development process. It is especially useful for repetitive coding tasks, debugging, and learning new programming languages or frameworks.
ChatGPT, with its broader language understanding and generation capabilities, is applicable in a wider range of scenarios beyond software development. It can perform most of the coding tasks enabled by CoPilot, although it may require specific prompting and may provide less relevant responses in some cases. Beyond coding, ChatGPT has multiple applications such as content creation, education, and data analysis.
2. Data Sources
The different data sources used for GitHub Copilot and ChatGPT influence their functionality and output. GitHub Copilot is trained on a large corpus of publicly available code from GitHub, encompassing a wide range of programming languages and coding styles. This extensive dataset enables it to suggest code snippets that are relevant and practical for a variety of programming tasks.
ChatGPT is trained on a diverse dataset that includes books, websites, and other textual sources. This training gives it a broad understanding of human language, enabling it to engage in detailed conversations, answer questions, and generate text on numerous topics. The diverse nature of its training data allows ChatGPT to be more adaptable and versatile in non-code-related contexts, but might cause it to provide less focused or lower quality responses for code-related queries.
3. Accuracy of Response
Accuracy in GitHub Copilot and ChatGPT is context-dependent. For GitHub Copilot, accuracy is measured in terms of the relevance and correctness of the code suggestions it provides. Given its training on a large corpus of code, Copilot is adept at offering accurate and syntactically correct code snippets. However, the suitability of these suggestions can vary based on the complexity of the coding task and the specificity of the user's requirements.
ChatGPT's accuracy is evaluated based on its ability to understand and respond to a wide range of queries and prompts in a coherent and contextually appropriate manner. While it excels in generating human-like text and maintaining the flow of conversation, its responses require verification for factual accuracy. Code generated by ChatGPT requires closer review, and in some cases might use incorrect syntax or even make up functions or libraries that don’t exist.
GitHub Copilot is designed to integrate seamlessly with coding environments. It is available as an extension in Visual Studio Code, Visual Studio, Vim, Neovim, JetBrains, and Azure Data Studio. This allows developers to use Copilot directly within their primary coding interface, enhancing the coding experience without needing to switch context.
ChatGPT does not support common IDEs out of the box. However, it offers broader integration possibilities due to its API. It can be integrated into various applications, websites, and platforms to provide conversational AI capabilities, including code generation. This flexibility makes ChatGPT suitable for a wider range of applications.
GitHub Copilot's main security concern revolves around the code it generates, particularly the inadvertent inclusion of proprietary or sensitive code from its training data. Users must be cautious about the code suggestions, ensuring they do not violate any licensing or confidentiality agreements.
For ChatGPT, security concerns are more about data privacy and the potential misuse of the model for generating harmful or biased content. Since ChatGPT processes a wide range of textual data, ensuring user privacy and controlling the type of content it generates are key security considerations.
6. User Interface
GitHub Copilot’s UI is designed to be intuitive for developers, providing code suggestions in a manner that feels like a natural extension of the coding process. The interface is unobtrusive, blending into the existing coding environment to enhance productivity without distraction.
ChatGPT’s default interface is quite simple, allowing users to provide natural language prompts, which can be up to thousands of characters in length and can include either code or text. It allows the user to carry out a prolonged conversation, and remembers earlier questions and responses. When used via its API, the implementation depends on how it is integrated into various platforms and applications. ChatGPT can be specifically integrated into development environments and processes, but this requires an extra effort.
GitHub Copilot uses a subscription-based pricing model. At the time of this writing, there are three pricing plans:
- Basic: $10 per month
- Business: $19 / user / month, includes additional features like code completions, chat, CLI assistance, and a security vulnerability filter
- Enterprise: $39 / user / month, includes additional features like chat personalized to your codebase, documentation search, pull request summaries, and fine-tuned models.
ChatGPT has the following pricing plans:
- Basic: Free, includes GPT 3.5.
- Plus: $20 per month, includes GPT 4 and advanced tools.
- Enterprise: Pricing not publicly available, includes additional security features, large-scale user management, and model customization.
In addition, OpenAI offers an API for ChatGPT 3.5 and 4. At the time of this writing, the cost of the latest version of GPT (GPT-4 Turbo) is $0.01 / 1K tokens for inputs and $0.03 / 1K tokens for outputs.
GitHub Copilot vs. ChatGPT: How to Choose?
When deciding between GitHub Copilot and ChatGPT, consider their distinct functionalities and how they align with your needs.
GitHub Copilot is tailored for software development. It suggests lines or blocks of code as you type, understanding the context of your code to fit your coding style and project structure. Integrated directly into your editor and supporting several programming languages, it's suitable for both novice and seasoned coders. However, its effectiveness varies with the task complexity and language used, and its code requires careful review.
ChatGPT is more versatile, handling a broader range of topics and conversations. It can accept more complex instructions that include both code and text, and can perform coding-related tasks that are outside the scope of GitHub Copilot. However, it is prone to factual inaccuracies and might provide less relevant or high quality responses for coding tasks.
A common choice is to use GitHub Copilot for focused software development assistance and ChatGPT for unstructured tasks, developer education, and creation of documentation or coding tutorials.
Related content: Read our guide to Copilot vs Tabnine
Complementing GitHub Copilot or ChatGPT with Swimm
Accurate answers require accurate context.
GitHub Copilot Business gives general answers to developer questions because it draws its context solely from the code editor. GitHub Copilot Enterprise promises more context-aware conversations with an additional layer of customization using your own repositories. But Copilot’s context is still limited to your code, and code alone cannot tell the whole story. With Swimm, all organizational knowledge sources are contextualized to provide highly accurate answers.
Documentation is an indispensable tool when it comes to enhancing the effectiveness of the responses generated by AI coding assistants. Well-written and up-to-date code documentation serves as a bridge, connecting the world of coding with the intuitive understanding of natural language, and therefore empowers AI Coding Assistants to function more effectively and accurately.