Since you're visiting our blog, you know that at ToolTime.tech, we love sharing how we work under the hood—from our thoughts on microservices and saying goodbye to Webpack in favor of ESbuild, to why we swapped "Tech Debt" for "Tech Time". This time, let's tackle a topic currently electrifying the entire IT industry: Artificial Intelligence and its role in day-to-day coding.
To many outsiders, it seems that with AI, you can just sit down, describe your dream system in one sentence, and with coffee in hand, watch the code write itself. The truth is, however, that as engineers, this approach makes us hit a brick wall very quickly.
Why does this happen? Because adopting AI in our daily work to build real features doesn't exempt us from thinking. Quite the opposite—it forces us to return to the absolute fundamentals of our craft. Here is how problem decomposition and iron-clad engineering principles turn AI into a powerful multiplier of our capabilities, rather than a chaos generator.
Good Practices Are Your Safety Net
At ToolTime, we know well that architecture matters. When you use AI as an accelerator, solid engineering practices are absolutely crucial to keep you from drowning in your own tech debt. Artificial intelligence can write absolute nonsense with terrifying confidence. How do we protect ourselves?
- Well defined, small tasks: The most difficult part to get right is to translate what the people want to how computers see the world. The more vague the instructions are, the more chances that the product will kind of work.
- "add users to the product" - sure I think passing email address in the header to authenticate them is a good idea, btw I will make sure all can drop tables,
- "add sidebar to the page" - of course, I will also make sure each click opens it fully.
Sometimes working with an agent feels similar to the stories about the evil genie from a lamp that interprets the wishes the way it wants. Keep it precise enough or you will suffer. - Automated tests: When writing a new feature in small steps, your verification mechanism isn't whether the code "looks reasonable," but whether it passes the tests. Techniques like TDD (Test-Driven Development) take on a whole new meaning with AI. First, define the system's behaviour in a test, then ask the model for an implementation that makes that test go green.
- Clean code and conventions: Language models are essentially powerful pattern-matching machines. If your codebase is built on SOLID principles, features clear naming conventions, and has a great structure, the AI will easily adapt to that style. However, if your project is "spaghetti," the model will happily pour another layer of sauce over it. Again, think of the LLMs as a multiplier. Got it ? Chaos in, more chaos out, well rounded code, patterns and principles in... gold out.
- Author is still responsible: The Engineer is the Author; the AI is the Ghostwriter. Author are 100% responsible for every line of code committed and it does not matter which tools you have used.
Building Blocks
When we combine task decomposition with engineering rigor, the process of building new features becomes incredibly smooth. It's like assembling a complex structure out of perfectly fitted building blocks.
We start with architectural work—understanding the domain and dividing the task. Then, using AI, we iteratively write small pieces of logic, hooks, endpoints, or database queries. AI dramatically speeds up writing boilerplate and frees us from having to manually scour API documentation. We deliver value faster because we can focus on what truly matters: how the individual blocks interact with each other.
The Craftsmanship Remains Key
The adoption of AI in our industry is no excuse to forget about engineering craftsmanship. On the contrary, it makes a deep understanding of architecture, clean code, and agile methodologies more valuable than ever.
Artificial intelligence effectively lifts the burden of routine off our shoulders, but we—the engineers—remain the ultimate decision-makers and architects. The next time you open your IDE with an AI assistant plugged in, don't ask it to build the whole engine. Ask it to help profile a great cylinder, and then use your engineering experience to start that engine.
