Ensuring Consistency in AI Code
Your AI code assistant just created a third way to validate emails.
Don’t assume AI-generated code is consistent. Code agents are shockingly good at establishing a pattern, writing code for it, then ignoring it to implement an entirely different way.
This creates a mess beyond simple duplication. You end up with many similar-but-different behaviors used arbitrarily across different parts of your application. One function might use your established validateInput() helper while another generates inline validation logic. One component might follow your error handling pattern while the next implements its own custom approach.
The result isn’t just bloat—it’s a maintenance nightmare for both humans and AI agents. You literally have created twice as much code to learn. Bug fixes and code reviews become the least fun game of Clue ever as you try to figure out which code block “done it”.
What to watch for:
- “Wait, didn’t we already have a function that does this?”
- “Didn’t I just explain how our validation works?”
- “Why is it creating so much code?”
- “Why is the reasoning so circular?”
Treat consistency as a first-class concern. Instruct the agent to analyze the existing codebase before implementing. Have it spell out the steps it will take to make a change and review them. Instruct it to validate changes with tests and to make its steps transparent to you - and read the transcript as it goes. Interrupt and ask if it is creating redundancy when you suspect it. Ask it to review its implementation for duplication after it says it is done.
Your future self debugging a production issue at 2 AM will thank you.
