I'm a full-stack developer, and my company recently provided the team with Claude licenses to increase productivity. Since then, I've been using Claude Code for tasks such as generating database models, defining relationships, creating controllers, building request validators, and following framework conventions. Work that previously took hours of documentation searches and manual coding can now be completed in minutes with a few iterations.
I don't blindly accept the output. I review every diff, check the documentation, test the changes, and only approve code I understand. However, I'm writing less code myself and spending more time reviewing and validating AI-generated code. Is this a reasonable way to work, especially when the employer expects us to use AI, or is it likely to cause problems with skill loss and code quality?
4 Answers
That workflow sounds reasonable. The important distinction is whether you’re using AI to handle repetitive implementation or outsourcing your understanding entirely. Reviewing the diff, checking assumptions, testing the result, and being able to explain the code are still essential. The job is shifting from typing every line to planning, directing, and judging the implementation.
The real warning sign isn’t writing fewer lines; it’s approving more code than you can properly validate. AI-generated changes should still have clear ownership, focused diffs, useful tests, and normal production monitoring. If coding time drops while review and QA remain serious, that can be a productivity improvement. If both coding and review time disappear, you’re probably accumulating unexamined technical debt.
Skill atrophy is worth taking seriously as well. Make sure you occasionally work through problems manually and stay familiar enough with the codebase to investigate failures when the generated solution is wrong.
Use deterministic tools for deterministic work whenever possible. If a framework already has a generator or a script that reliably creates model classes, let Claude invoke or guide that tool instead of asking it to recreate the output manually. That tends to be more consistent, cheaper, and less vulnerable to subtle omissions.
AI is excellent for boilerplate, repetitive refactors, migrations, debugging help, and learning unfamiliar APIs. It is much less dependable at architecture and feature design, especially when requirements may change. Break larger features into small, well-defined pieces, agree on the design with your team first, and have the tool implement only what you can review and test.
That’s how I approach feature work too: settle the design first, then use AI for individual components. Smaller changes are easier to understand, test, and correct than asking it to build an entire feature in one shot.
The main thing I’d watch is whether you could still debug the code without the tool if necessary. If you can, you’re probably using it as leverage rather than replacing your own understanding.