Monday Snack: Keep the creative soul alive
Let’s be real about the pressure to hand over entire codebases and UI layouts to AI, especially since the results usually look incredibly generic.
It is easy to notice a growing trend of repetitive layouts across the web. You click on a new landing page, and it often features the same structure, the same generic UI cards, and the exact same AI-generated testimonial placeholders.
When every system relies on the same training data, output becomes deterministic and predictable. It represents the most probable next step, but not necessarily the most creative or effective one.
Standard Patterns vs. Real UX
As developers, our responsibility extends beyond making a interface look functional out of the box. We have to focus on maintainability, state management, and user experience (UX).
When you ask an AI tool to generate a feature from scratch, it can produce standard code or layouts quite easily. However, it doesn't understand the specific context, the subjective logic, or the precise user flows unique to your application. If we offload the actual structural thinking to automated prompts, we often introduce consistency issues, technical drift, and a flat user experience.
But if we use it strictly to eliminate repetitive boilerplate? That is where the tool becomes genuinely useful.
Automating the tedium
Using AI as an assistant for routine production tasks saves an immense amount of time.
Consider a common testing scenario: you just finished writing a new Angular service, and now you need to write 15 variations of the exact same unit test pattern. The traditional manual process involves setting up the TestBed, creating mock data objects, spying on dependency methods, and duplicating it() blocks for every single API success and failure state.
Instead, you can pass your completed component or service file to an LLM alongside your team's preferred test template and instruct it to generate the standard test boilerplate. Because the code is already written, the system has a strict set of rules to follow. The tool handles the execution perfectly in seconds, delivering a clean spec file with your mocks, setup, and basic assertions ready to run.
We can apply this exact mindset to writing our code. Instead of asking AI to architect the data flow of a feature, let it generate the predictable, repetitive unit tests based on your established testing patterns.
Balancing the tool and the craft
Software engineering isn't defined by how quickly we type boilerplate or repeat established workflows; it is about solving problems for the person on the other side of the application.
Using automation to clear away predictable tasks, like scaffolding standard properties, writing basic CSS variables, or creating tests, keeps our documentation and workflows efficient. But when it comes to the core system architecture, the user flow, and the critical decisions, keeping your hands on the keyboard is what ensures a high-quality product.