You opened your IDE, pasted in a prompt, and within ten minutes you had a working app. Then you kept going, and soon enough it became a problem.
LLMs have improved. The structural problem hasn't.
In recent years language models have taken a clear leap: they no longer just write small, isolated functions, they reason about sizeable chunks of code. That is real progress, not marketing.
But the structural limits remain. And ignoring them is the fastest way to end up with an unmanageable codebase.
What the context window is and why it holds you back
What is the context window in an LLM? It is the maximum amount of text a model can "keep in mind" during a session. Beyond that threshold, the model loses the information it accumulated earlier.
In practice: on a small project, the AI knows all the code. On a project that has grown over time, it starts reasoning on fragments. It doesn't see the overall architecture, it doesn't remember the decisions made three files ago, it doesn't connect the dependencies.
The result? Every change is technically consistent with the piece it can see, but inconsistent with the rest of the system.
The silent technical debt of AI
There is a second problem, less obvious but just as damaging.
When an LLM meets a case it wasn't expecting (an edge case, a complex interaction between components, an ambiguous requirement) it tends to sketch a solution that works in the short term. Not because it is stupid: it is the expected behaviour of a system trained to complete text plausibly.
These "compromise" solutions pile up. One after another, silently, they degrade the quality of the architecture. You don't notice until adding a feature becomes a risky operation, and fixing one bug creates two more.
This is technical debt. Generated quickly, but identical in its consequences to the kind built by hand.
How to mitigate it, and how we work
When is it worth using static analysis tools with AI? Always, from the very start. Typecheckers and linters give the LLM immediate, structured feedback on the generated code, reducing the errors that slip through the context window. The model corrects itself in real time, instead of propagating silent errors.
But tools alone are not enough. You need people who can recognise when generated code is technically valid but architecturally wrong. That is a different skill from writing code: it is knowing how to evaluate code produced by a system that does not understand your project as a whole.
At Quinck we have built a framework, constantly evolving, that integrates static analysis, structured review steps and workflows designed to work with AI tools in a controlled way. It is not a magic formula: it is a process we update every time we hit a new limit.
The point is not to use AI less. It is to know where it stops being reliable, and to guard that boundary.
