Hey everyone! I've noticed a trend where many teams, including some friends and ex-colleagues, are increasingly relying on AI tools to generate code. While it's incredibly fast and feels almost magical, it raises some red flags about security, scalability, and maintainability. From my freelancing days, I've seen codebases that are great at first but turn into nightmares down the line because nobody properly reviewed what the AI produced. This can lead to security bugs and messy structures that don't scale well.
So, for those of you who are currently involved in coding or reviewing code, I'm curious:
• Do you have a process for evaluating AI-generated code in terms of security, scalability, maintainability, and modularity?
• If so, what's working for you? Is it just manual reviews, automated tools, CI/CD scans, or something else?
• If not, what would you like to see that could help make this easier?
• And for those who are shipping products quickly with a lot of AI support, what are your methods for ensuring your code stays scalable and secure?
I'd love to hear your stories, frustrations, or wishlist ideas. 🙌
4 Answers
I can totally see coding evolving into more of a review and debugging role in the future. It sounds quite daunting to me! 😅 It feels like we've already been doing this to some extent with lower-budget projects needing rescue work. Not sure I want that to be the norm!
I understand your feeling. It may seem gloomy, but I see it as a chance for more specialized roles, turning into something interesting!
Before I even start coding, I lay everything out in advance—architecture, security handlers, and patterns for specific situations. If I hit a snag, I might consult ChatGPT for solutions, but I don't give it my actual working code. I ask questions without revealing my business logic, which keeps my code separate.
The main issue seems to lie in the lack of code reviews regardless of whether it’s AI-generated or human-written. Companies should always prioritize meaningful code reviews and testing like regression, unit, and integration. It's vital for ensuring code quality and scalability. If you want a solid system, you HAVE to load test it. A strong engineering culture that promotes guidelines is key to avoiding messy code.
With decades of experience under my belt, I’ve been experimenting with AI code. Crafting the right prompts for AI like ChatGPT-5 to create functional and secure code requires knowledge. Reviewing the generated code takes time, but I appreciate how closely it mimics my style. I typically start with a comment and let the AI fill in the code bit by bit, which is manageable for review.
What led you to think it’ll become like that? I’ve actually been handling code that many others wrote before, and I don't find it miserable at all.