AI in Programming: ChatGPT, GitHub Copilot and Alternatives

In today's programming world, many tools promise to improve code efficiency and quality. ChatGPT and GitHub Copilot are currently the two most useful AI assistants that we use in our daily work. While these tools have their merits, it's worth looking at whether they really meet all expectations and to what extent they can be useful. As the market for AI tools grows, it is worth considering whether their functionality actually translates into benefits, or whether they have their limitations.

Actual value vs. expectations

AI assistants such as ChatGPT and GitHub Copilot have grown in popularity, promising to improve efficiency and code quality. Despite their growing importance, it's worth asking whether these tools actually deliver on the promises we attribute to them. On the one hand, they can speed up the coding process and help solve problems, but on the other hand, there is a risk that their real value may not be as great as they seem. It is crucial to understand how these solutions affect developers' day-to-day work, what specific benefits they bring, and what limitations may exist. In this context, it is also worth looking at other tools, such as Tabnine and Claude.ai, to assess how they compare to ChatGPT and GitHub Copilot in practice.

Utility of AI-based tools

The purpose of this article is to provide practical insights into the usefulness of two AI tools - ChatGPT and GitHub Copilot - in the context of a programmer's daily work. Instead of focusing on the general promises that often accompany new technologies, I will try to analyze their actual impact on coding efficiency and problem solving. From the perspective of my experience, I will assess what specific benefits these tools bring, as well as what limitations they may have. In addition, I will briefly mention other tools such as Tabnine and Claude.ai to get a broader picture of the options available and see how these solutions compare to ChatGPT and GitHub Copilot.

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This article aims to provide a personal analysis of the ChatGPT and GitHub Copilot tools that I find most valuable in my programming work. I will focus on evaluating their actual usability, functionality and impact on daily coding tasks. I will discuss what the benefits and challenges of using them are, and whether they really improve the efficiency of a programmer's work. In addition, I'll take a brief look at other tools, such as Tabnine and Claude.ai, to see how they compare with ChatGPT and GitHub Copilot, and what place they have in the broader context of AI support in programming.

ChatGPT and GitHub Copilot - Experiences and Tool Selection

As a full-stack developer who works with C# and Angular on a daily basis, I use several AI tools to increase my efficiency. Two of them, ChatGPT and GitHub Copilot, offer different features that support my work in different ways. In this section, I will describe why I use both of these tools, their strengths and weaknesses, and how each supports me in my daily tasks.

Why do I use both tools?

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I use both ChatGPT and GitHub Copilot because each offers different benefits that I find useful in different contexts:

• GitHub Copilot: It is more effective at prompting small pieces of code that I am currently writing. Thanks to its integration with the IDE, Copilot can predict what I would like to write next, which speeds up the coding process considerably. I mainly use it for quick additions and automating daily coding tasks.
• ChatGPT: It is more versatile and effective for cases where I need support for larger functionalities, bug analysis, or quick research. As a tool for discussing and bouncing ideas, ChatGPT helps me develop larger pieces of code and test different approaches.

Applications and strengths

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GitHub Copilot

• Code hinting: Great for suggesting small pieces of code, making it easy to quickly implement known patterns and methods.
• IDE integration: Automatic suggestions in the IDE help with ongoing code writing, which saves time and increases productivity.

ChatGPT

• Create larger features: Better for designing and creating larger chunks of code and for considering different approaches to solving a problem.
• Error analysis: Helps debug problems by providing clues and quick information that you would normally have to find manually.
• Research and documentation: I use ChatGPT for writing simple, time-consuming SQL queries, large switch structures or creating documentation where standard Copilot functions are less effective.

Practical differences and limitations

• ‍‍GitHub Copilot: often overlooks contextual needs, especially when I'm not sure exactly what functions I want to write. Sometimes corrections are needed, but Copilot effectively speeds up the code development process.
• ChatGPT: While ChatGPT is a great support for analyzing code and finding solutions, there are times when its answers can be inaccurate or even wrong (“hallucinatory”). For example, I happened to lose several hours to erroneous suggestions, a reminder that while the tool is useful, it requires caution and verification.

ChatGPT and GitHub Copilot play different roles in my work as a programmer. Copilot is invaluable for day-to-day code writing and automation, while ChatGPT works better for problem analysis, creating larger features and research. Each of these tools has its own strengths and limitations, which must be taken into account in order to effectively support the software development process.

Alternatives to ChatGPT and GitHub Copilot - Tabnine and Claude.ai

In the previous section, I discussed how I use ChatGPT and GitHub Copilot in my daily work as a developer. Now we'll take a look at two other tools that may be alternatives: Tabnine and Claude.ai. While my experience with them is limited, I've looked at their features and capabilities to see how they might compare to my current choices.

Tabnine - An alternative to GitHub Copilot

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Tabnine is a code autocomplete tool that works similarly to GitHub Copilot. It integrates with popular code editors, providing code suggestions based on context and previous code.

• Strengths:
  Personalization: It adapts to the user's coding style, which can improve the precision of suggestions.
  Multi-language support: Supports a wide range of programming languages, making it a versatile tool.
• Limitations:
  Less advanced analysis features: Focuses mainly on code autocomplete, offering no support for error analysis or creating more complex code snippets.

Claude.ai - An alternative to ChatGPT

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Claude.ai is a tool focused on natural language processing, much like ChatGPT. Although less known in a programming context, it offers advanced text generation and analysis capabilities.

• Strengths:
  Advanced language processing: Can be useful for documentation creation, code analysis and quick research.
  Flexibility: Enables a variety of natural language applications, which can support different aspects of a programmer's work.
• Limitations:
  Lack of dedicated coding features: May not offer features such as code autocomplete or specific debugging support like ChatGPT.

Although Tabnine and Claude.ai are interesting alternatives to ChatGPT and GitHub Copilot, they personally have not convinced me enough to consider using them permanently. Tabnine, while versatile in code autocomplete, offers nothing more than GitHub Copilot, and Claude.ai, with its advanced natural language processing, does not provide the full range of features that are crucial to me in my daily programming work. Therefore, for the time being, I remain with ChatGPT and GitHub Copilot, which better suit my needs and work style.

How do AI-based tools actually affect work?

In the article, I discuss how AI tools such as ChatGPT and GitHub Copilot affect my daily work as a programmer. For me, ChatGPT and GitHub Copilot are key assistants that significantly improve coding and troubleshooting efficiency. GitHub Copilot does a great job of prompting code as I write, while ChatGPT is invaluable for analyzing larger chunks of code and supporting debugging.

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On the other hand, although Tabnine and Claude.ai offer interesting features, they have not convinced me enough to consider using them on a permanent basis. Tabnine, despite its versatility, does not bring significant innovations compared to GitHub Copilot, and Claude.ai, while advanced in natural language processing, does not provide coding features that are crucial to me.

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In conclusion, ChatGPT and GitHub Copilot remain my main coding support tools, offering me real benefits and efficiencies. When considering putting new AI tools to work, it's worth thinking about what features are most important to you and what tools best suit your needs.
I encourage you to test different options and draw your own conclusions. As AI technology in programming evolves, it is possible that new, even more advanced tools will emerge that could revolutionize our approach to coding.