Why AI often fails on real software projects — and how I avoid it
AI tools write code fast, but they forget your project's details even faster. The fix isn't a long instructions file — it's an information system that keeps up with the project.
The problem most people hit when using AI coding tools
You ask an AI coding assistant to do a task, and it writes code that looks correct — but it conflicts with a decision you made last week, or ignores an important rule in your project. The reason: the tool doesn't actually "remember" your project; it's working from outdated or incomplete information.
The common (and insufficient) fix
Many people write one long instructions file describing the project and rely on it. But that file goes stale fast — the code changes, nobody updates the file, and the same problem returns a week or two later.
What I did instead
I treated "the information AI needs about your project" as a core part of the product, not an afterthought:
- **Clearly separated information**: major design decisions live in one place, domain rules (like "every payment must be logged") in another, and the current task's status in a third — instead of mixing everything into one file.
- **One source of truth**: a single trusted place for requirements and tests, so nobody — human or AI — works from an outdated or conflicting version.
- **Mandatory updates**: if an important part of the code changes, an automated check blocks the work until the related information is updated — so it never quietly goes stale.
How a project actually moves from idea to production
1. You start with a clear description of what you want to build.
2. That description becomes small tasks that can each be tested independently.
3. Each task is given to the right tool along with exactly the information it needs — no more, no less.
4. Careful human review at any sensitive point (like security or data privacy).
5. Launch happens gradually with results monitored — not all at once.
Why this matters to you as a client or visitor
Because the difference between an AI-powered project that works reliably and a "demo" that keeps breaking is exactly this system. Speed only helps when it's built on a foundation you can trust.