ChatGPT vs Copilot: Which And When To Use
Did you know that nearly 90% of people prefer doing their work by others? We all strive to delegate tasks and focus on more important aspects of our work. Luckily, in software development, artificial intelligence has introduced a game-changing solution: AI code-writing assistants.
GitHub Copilot and ChatGPT are the leading artificial intelligence code writing tools. While both possess the capability to enhance writing efficiency and provide code snippets, they exhibit unique strengths and cater to diverse purposes. In this article, we will dive deep into their features, capabilities, and use cases, empowering you to make an informed choice when deciding between GitHub Copilot and ChatGPT.
Let’s dive in!
What is GitHub Copilot?
GitHub Copilot is an advanced AI-powered code completion tool that has taken the developer community by storm. Leveraging the power of OpenAI's GPT-3 language prediction model, Copilot analyzes code context and suggests lines of code to enhance your coding experience.
How GitHub Copilot Works
GitHub Copilot provides machine learning capabilities to understand the context of your code and provide intelligent suggestions for code completion. It excels when used from the beginning of a project, generating functions, variable names, and algorithms that align with your programming style. By learning from the code you write, Copilot continually improves its suggestions over time.
GitHub Copilot supports various programming languages, enabling developers to utilize its functionality across various projects. Some of the supported languages include:
Use Cases of GitHub Copilot
GitHub Copilot offers a multitude of use cases, empowering developers to write code faster and more efficiently. Some specific use cases of Copilot include:
- Autocomplete code suggestions to expedite coding and reduce manual typing.
- Intelligent function and variable suggestions allow developers to focus on high-level logic rather than boilerplate code.
- Efficient code refactoring and optimization suggestions, enhancing code quality and readability.
- Simplifying complex algorithms and data structures by providing well-documented code examples from GitHub's extensive repository.
Key Features of GitHub Copilot
- Vast code repository and access to real-world examples: Copilot taps into GitHub's vast codebase, providing developers with relevant and well-documented code examples. This feature assists developers in quickly understanding and implementing complex functionalities.
- Excellent understanding of programming idioms and patterns: Copilot's AI model has been trained on millions of lines of code, enabling it to grasp common idioms and patterns.
- Сomprehensive code snippets: Going beyond individual code lines, Copilot offers entire functions or classes as code snippets. This comprehensive approach accelerates the development workflow, enabling developers to streamline their coding tasks.
- AI-based code suggestions: GitHub Copilot leverages artificial intelligence to provide intelligent code suggestions based on the context of your project and adherence to coding conventions. It assists developers in writing code faster and more accurately.
- Faster coding: GitHub Copilot enables developers to write code faster by providing instant code suggestions as they type. This feature enhances productivity and reduces manual typing.
- Direct integration: GitHub Copilot integrates with popular code editors and IDEs, such as Visual Studio Code, JetBrains IDEs, and Neovim. It provides suggestions directly to the editor, ensuring a smooth coding experience.
But you're probably wondering whether GitHub Copilot has any limitations. The. simple truth is yes, this tool may not meet all our needs in the development process but we can explore all challenges and better overcome them. So let me explain the main limitation of GitHub Copilot.
Reliance on existing code patterns
GitHub Copilot heavily relies on existing code patterns and examples available in its training data. While this ensures contextually accurate code suggestions, it may also limit developers' creativity and hinder their ability to explore innovative approaches or think outside the box. Developers should be cautious not to blindly follow Copilot's suggestions without critically evaluating their suitability for their use case.
Limited support for certain programming languages
What is ChatGPT?
ChatGPT is a universal LLM (Large Language Model) for various tasks developed by OpenAI. It leverages OpenAI's large language models, such as GPT-3.5 and GPT-4, to generate text and content based on provided prompts. ChatGPT can perform various tasks, including generating code snippets, writing articles, answering questions, and more. It has been trained on vast data to understand human language and produce responses that resemble human writing.
How ChatGPT Works
ChatGPT works in a chat-based manner. Users provide detailed prompts or questions, and ChatGPT generates text in response. The model is designed to simulate human-like conversations and can handle follow-up questions, admit mistakes, challenge premises, and reject results. However, it's important to note that ChatGPT's knowledge is based on events that occurred until 2021, so it may not provide accurate information about events after that year.
Use Cases of ChatGPT
ChatGPT has a wide range of use cases across various domains, including:
- Code Generation: Developers can use ChatGPT to generate code snippets, functions, or even entire classes for different programming languages. It can help automate repetitive coding tasks and provide assistance in implementing specific functionalities.
- Chatbot Integration: ChatGPT can be integrated into chatbot applications, enabling developers to create conversational agents with human-like responses. It can enhance user interactions, answer queries, and provide assistance in a wide range of applications.
- Code Refactoring and Optimization: ChatGPT can help developers improve the quality and efficiency of their code. Providing specific code snippets or suggestions can assist in refactoring existing code to make it more readable, maintainable, and performant.
- Bug Fixing and Troubleshooting: When encountering bugs or issues in their code, developers can turn to ChatGPT for assistance. By describing the problem and providing relevant code snippets, developers can receive suggestions and potential solutions to help debug and troubleshoot their code.
- API Integration: Integrating APIs into applications often requires understanding the API documentation and implementing the necessary code. ChatGPT can assist developers by generating code snippets demonstrating how to interact with specific APIs, making the integration process smoother and more efficient.
- Rapid Prototyping: ChatGPT can be a valuable tool for rapid prototyping. By providing a high-level description of the desired functionality, developers can quickly receive code snippets or suggestions that help them build functional prototypes of their applications, saving time and effort in the early stages of development.
- Framework and Library Usage: When working with unfamiliar frameworks or libraries, developers can leverage ChatGPT to receive guidance and examples on using specific features or functionalities. This can help developers quickly grasp these tools' usage patterns and best practices, enabling them to work more effectively with new technologies.
- Cross-Language Code Migration: Moving codebases from one programming language to another can be challenging. ChatGPT can aid developers in this process by suggesting code translations or providing equivalent code snippets in the target language, facilitating the migration and reducing the effort required for language-specific adaptations.
Key Features of ChatGPT
- Code snippets: ChatGPT goes beyond generating isolated code lines by providing comprehensive code snippets encompassing entire functions, classes, or modules. This capability allows developers to accelerate their workflow and streamline development tasks.
- Natural language understanding: It can accurately interpret descriptive prompts and provide relevant code suggestions tailored to the context.
- Versatile AI Capabilities: ChatGPT, powered by OpenAI's transformer-based architecture, offers a wide range of AI capabilities beyond code generation. It excels in natural language understanding, chat interactions, language translation, answering questions, and image interpretation. This versatility makes it an invaluable tool for developers and content creators.
- Code Generation Assistance: ChatGPT can assist in generating code snippets and guide the coding process. While it may not be as specialized as GitHub Copilot in code generation, ChatGPT's ability to understand and interpret code-related queries allows it to provide relevant suggestions and help developers in their coding endeavors.
- Seamless Integration: ChatGPT can be easily integrated into various applications and platforms through its API. This allows developers to incorporate AI capabilities into their software or systems, enhancing their applications' functionality and user experience.
Limitations of ChatGPT
Keep reading to discover the main challenges of ChatGPT in coding.
- Limited Understanding of Programming Languages: ChatGPT's knowledge of programming languages is based on the training data it has been exposed to. It may not know the latest language features, libraries, or frameworks. This can lead to outdated or suboptimal code suggestions, especially when working with cutting-edge technologies or niche programming languages.
- Inconsistencies and Errors: ChatGPT's responses are generated based on patterns and examples in the training data. It can occasionally produce incorrect or inconsistent code suggestions due to the limitations of the training process.
- Lack of Domain-Specific Knowledge: ChatGPT's training data includes various sources, including online text and publications. However, it may not have access to specialized or industry-specific knowledge crucial for writing code in certain domains.
- Dependency on Training Data: ChatGPT's performance is highly dependent on the quality and diversity of its training data. If the training data contains biases or inaccuracies, it can affect the generated code and written content. OpenAI continually works to address these issues, but it remains an ongoing challenge.
Comparison of ChatGPT vs GitHub Copilot
So now let's dig a little deeper at the comparison of certain cases between ChatGPT vs GitHub Copilot.
Use Cases and Applicability
When considering the use cases and applicability of ChatGPT and GitHub Copilot, it becomes evident that each tool has its strengths and is better suited for specific scenarios. Let's explore these scenarios in detail:
ChatGPT demonstrates its value in various situations where developers require assistance with general-purpose coding tasks. Some key use cases include:
- Boilerplate code generation
- Algorithm implementation
- Data transformations
GitHub Copilot excels in specific situations where its strengths, such as a vast code repository and an understanding of established programming patterns, are highly valuable. Here are some notable use cases:
- Access to real-world examples
- Programming patterns and idioms
- Specialized domains and libraries
The choice between ChatGPT and Copilot often hinges on the specific requirements of the development project. Consider the following examples:
- Startup rapid prototyping: ChatGPT's versatility and ability to generate code with explanations may be more beneficial for a startup focused on rapid prototyping and experimentation. It provides a broader range of support across various coding tasks, allowing developers to iterate and explore different ideas quickly.
- GitHub repository integration: When a development team relies extensively on GitHub repositories for code collaboration and version control, GitHub Copilot's seamless integration with GitHub becomes a significant advantage.
- Project-specific requirements: The choice between ChatGPT and Copilot can depend on the specific requirements of a project. For example, ChatGPT's capabilities in generating human-like text may be more valuable if a project involves primarily text-based natural language processing. Alternatively, if a project heavily relies on existing code patterns and libraries, Copilot's expertise in leveraging established programming patterns becomes a determining factor.
By conducting a thorough evaluation, we can gain insights into the effectiveness and reliability of the code generated by ChatGPT. Some evaluation methods may involve:
- Code efficiency: Assessing the performance and computational complexity of the generated code to ensure it meets the desired efficiency standards.
- Code readability: Evaluating the clarity and comprehensibility of the generated code, considering factors such as variable naming, code structure, and overall organization.
Examining the code generated by Copilot is crucial to understanding its performance in terms of code generation and comprehension. Evaluation methods may include:
- Code Accuracy: Verifying the correctness and reliability of the code generated by Copilot through testing, debugging, and code review processes.
- Relevance to Context: Assessing how well Copilot understands the context and requirements developers provide, ensuring that the generated code aligns with the intended functionality.
- Completeness of Solutions: Evaluating whether Copilot offers comprehensive and robust code snippets that cover all necessary aspects of the given coding task.
Both ChatGPT and Copilot may encounter performance limitations or face challenges in certain scenarios.
- ChatGPT may struggle with understanding abstract or ambiguous prompts and may find it difficult to generate code that meets strict security requirements or handles sensitive data appropriately.
- Copilot's performance may be limited when dealing with rare or specialized programming scenarios, where the availability of relevant real-world examples or established patterns is limited.
Let's compare the user interfaces of GitHub Copilot and ChatGPT:
GitHub Copilot offers a user-friendly interface that simplifies the coding process. By installing it as an extension in Visual Studio Code, users can seamlessly integrate it into their coding environment. The interface provides a smooth and intuitive experience for developers. Some key points to note about Copilot's user interface include:
- Easy Integration: The extension can be added to Visual Studio Code with ease, allowing users to access Copilot's functionality within their preferred coding environment quickly.
- Coding Versatility: Copilot enables users to write code for various purposes, from simple to complex tasks. With its AI-powered assistance, developers can accomplish various coding tasks efficiently.
- Cost-Effective Solution: Copilot offers a cost-effective alternative by reducing the reliance on in-house coders. The tool provides high-level coding capabilities at a nominal fee, potentially saving company costs.
ChatGPT also offers a user-friendly interface to assist developers in their coding endeavors. The UI of ChatGPT is clean and satisfactory, providing a seamless experience for users. ChatGPT's interface allows users to interact with the AI model and seek coding assistance ranging from simple to complex tasks. It offers a broad spectrum of coding support, catering to different difficulty levels.
GitHub Copilot and ChatGPT serve different purposes regarding code generation and assistance. Let's compare their capabilities using the same query in Python:
GitHub Copilot: The primary task of GitHub Copilot is to complete code and assist developers in boosting their productivity, especially with repetitive and simple code snippets. Copilot suggests concrete function implementations based on the existing codebase and project context. For example, if you need to implement a parseExpression() function, Copilot will analyze the structure and overall context of your project to provide suggestions on how to complete this specific functionality efficiently.
ChatGPT: On the other hand, ChatGPT is a chatbot framework that extends its assistance beyond code completion. While it can still help developers in the development process, ChatGPT's strength lies in its ability to help design large-scale systems and provide a roadmap for implementation. It can assist in tasks such as creating the syntax for a new programming language, building context-free grammar, and recommending steps for compiler implementation. ChatGPT takes a broader perspective and offers guidance on the architecture and implementation of complex systems.
When comparing the two tools using the same Python query, Copilot will focus on generating code snippets for specific functionalities. At the same time, ChatGPT will provide a more comprehensive approach, offering insights and recommendations on designing and implementing the entire system.
Accuracy of response
ChatGPT's responses are generally accurate, but it is important to note that it can be prone to errors based on outdated or incorrect data assumptions. While most of the time it provides accurate responses, there have been instances where it generated strange or inaccurate answers. However, OpenAI continually works to improve the model with each new version. For example, GPT-4 introduced a higher level of accuracy.
OpenAI has stated that GPT-4 is 82% less likely to respond to requests for content that OpenAI does not allow and 60% less likely to invent answers than its predecessor. Nonetheless, it's important to remember that perfection cannot be expected, including in coding. Therefore, it is crucial to verify the programming output suggested by ChatGPT using human eyes.
GitHub Copilot also faces accuracy issues, and users accept an average of 26% of all completions shown by the tool. In specific languages like Python, this acceptance rate increases to 40%. It is important to understand that GitHub Copilot does not generate perfect code. Instead, it strives to create the best possible code based on the available context. Overall, GitHub Copilot's code generation tends to be more reliable compared to ChatGPT's capabilities in coding tasks.
ChatGPT can be seamlessly integrated into other applications using its API. This enables developers to generate responses and converse with users within their software or platforms. Additionally, plugins are being developed to enhance the integration of ChatGPT with popular services like Kayak, Expedia, OpenTable, Slack, and Shopify, among others. ChatGPT also boasts integration with a wide range of programming languages.
GitHub Copilot is tightly integrated with several development environments, including Visual Studio Code, Visual Studio, Neovim, and JetBrains IDEs. This enables developers to use Copilot's code generation and completion features seamlessly within these IDEs. Copilot can analyze and draw from billions of publicly available code across multiple programming languages.
ChatGPT has not received extensive attention regarding security measures. Developers utilizing ChatGPT are responsible for incorporating their security features and considerations into the applications or systems they build. While OpenAI, the organization behind ChatGPT, emphasizes the importance of security, it does not provide specific built-in security features for the tool itself.
GitHub Copilot benefits from the robust security features implemented on the GitHub platform over the years. As a part of GitHub's ecosystem, Copilot leverages the security measures provided by GitHub to ensure a secure coding environment. For business users, Copilot offers additional coding privacy and protection measures.
One notable security feature of GitHub Copilot is its AI-based vulnerability prevention system. This system actively identifies and blocks insecure coding patterns in real-time. It targets common vulnerable coding patterns, such as hardcoded credentials, SQL injections, and path injections.
When comparing GitHub Copilot and ChatGPT, it's important to consider specific use cases. To make a decision, consider programming language preferences, project complexity, and team dynamics. Evaluate your projects' specific requirements and your team members' expertise. Experimenting with both tools and assessing their performance for your needs may be beneficial.
While these AI tools offer significant benefits, they are not flawless. ChatGPT and GitHub Copilot have limitations and may occasionally provide inaccurate or incomplete results. Therefore, reviewing and validating the output generated by these tools is essential, especially regarding critical tasks or sensitive code.
Embracing the symbiotic relationship between human intelligence and AI technology will enhance productivity, innovation, and success in software development.