AI Code Assistants: Final thoughts

As we approach the end of the series 'AI code assistants', it's time to reflect on the true potential of using such tools and all the challenges and responsibilities that using this technology presents.

This final post of the series is going to focus on the good, the bad, and the ugly aspects of AI code assistants, and why it's crucial to approach them with a balanced mindset.

The Good

I mean, there is no denying that AI is here to stay. The world is already bedazzled with what AI can achieve and in our field it's becoming undeniable that code assistants bring significant improvements to software development.

From speeding up the process by offering real-time code suggestions, completion, generating documentation, and even setting up infrastructure, developers of any level can make use of these tools to boost their productivity. Beginners, especially, benefit from having an AI assistant by their side, helping with the hardest part - getting started. Instead of being overwhelmed by complex concepts, they can leverage AI to lower the learning curve, generate sample code, and get functional solutions up and running faster.

Additionally, AI code assistants allow more senior developers to automate repetitive tasks, giving them the mental space to focus on more complex issues. Whether it’s refactoring large codebases, generating test cases, or writing boilerplate code, these tools can save hours of manual labor and becoming a good prompt engineer or a great curator of code can definitely help you become a better software engineer.

AI can act as a collaborative partner that speeds up workflows and helps developers at various stages of the development.

The Bad

However, and like all things in life, relying too much on something comes with some drawbacks. While these tools can generate code or suggestions fairly quickly, it doesn’t always mean that these outputs are perfect or correct. AI-generated code can be inaccurate, outdated, or not aligned with best practices. This becomes especially dangerous when developers treat the assistant’s outputs as an absolute truth.

For beginners, while AI can help them get started, it might also prevent them from diving deeper into core principles of programming and software architecture. Novice programmers won’t feel the pain of spending countless hours hunting down problems. This "shortcut" to learning can lead to developers who can produce code but lack the underlying knowledge to understand why the code works or how to troubleshoot it when things go wrong. They won’t see the dangers out of the blue or might fail applying design patterns successfully.

It’s quite common to fall into the trap of letting the AI do all the work, but this can lead to a shallow understanding of crucial concepts and end up with bad architectural decisions. Therefore, it’s critical for developers to analyse AI suggestions. Treat the AI as an assistant, a tool, not a replacement for your own expertise. Even if it helps write 90% of your code, that remaining 10% is where you review, refine, and test which can make all the difference in ensuring quality.

The Worst

Perhaps the most concerning issue with AI code assistants is the potential for hidden errors, bias, or security vulnerabilities in the code they generate. Keep in mind that AI models are often trained on vast amounts of data with outdated information. Carrying over outdated practices can lead to code that it’s inaccurate or even introduce bugs. If developers are not careful, these issues can lead into larger problems, especially in production environments.

Imagine an AI assistant suggesting an outdated encryption method or not fully accounting for edge cases in a function. The AI might not always recognise these issues, but as developers, we should. This brings us to two important points: prompt engineering and curation. The outputs you receive are only as good as the prompts you provide. Asking the right questions, being specific in your instructions, and curating the AI’s outputs is key to making sure the tool serves you well.

With the power AI brings, there is an added responsibility to use it wisely. Just because you have a tool that can generate code in seconds doesn’t mean that it's production-ready. We must review every line it produces, understand its implications, and ensure it meets both our project's requirements and ethical standards. With great power comes great responsibility, and nowhere is this truer than with AI-assisted development.

Conclusion: Finding the balance

I still remember the old days where IDE’s would replace every single text editor or when WYSIWYG tools would replace the need of designers and programmers, and guess what? They did not. AI assisted tools are undeniably powerful tools, but they are just that, tools. They still rely on a great level of knowledge, understanding and experience to be properly operated and they are not a substitute for human judgment.

As developers, we must remember that while AI can accelerate development it can also accelerate mistakes and knowledge gaps. AI can certainly help novice developers overcome the getting started friction but they should not replace the process of learning. True mastery comes from understanding the why and how behind the code.

The early bird catches the worm and the future is reserved for clever and skilled humans that acknowledge and are able to work with a great tool. A balanced approach involves using AI to handle tedious or repetitive tasks while maintaining both eyes and mind on its outputs.

As we integrate AI more into our workflows, we must ensure that we remain active participants in other coding process, constantly questioning, refining, and improving the work we are committed to deliver.

Stay critical, stay curious, and always be the one guiding the code.