Many teams notice that AI starts producing weaker or more inconsistent results the longer they work on the same project.

As more requirements, decisions, and details are layered in, the output becomes less precise. The model overlooks earlier instructions, shifts focus, or prioritizes the wrong things – not because the task has changed, but because the amount of information has grown beyond what it can actively handle.

This is not a sign that the model is unreliable. It is a structural condition of how AI systems work.

The explanation lies in one central concept: context windows.

Context windows define the limits of understanding

A context window is the total amount of information an AI model can actively take into account at any given moment.

All instructions, constraints, decisions, and examples the model should consider must fit within this window. When more information is added than the window can hold, something is pushed out. Not selectively and not intelligently, but as a technical necessity.

Some models attempt to summarize earlier interactions and maintain a degree of continuity. But they cannot independently determine which decisions are structurally important and which were only temporarily relevant. That responsibility belongs to the process around the model.

This is why issues often emerge only once a project grows and complexity increases.

What happens when context grows too large

In practice, the model begins working with an incomplete foundation.

Earlier decisions fall out of context. Assumptions are no longer applied. Stylistic or architectural choices are broken without the model “knowing” it is doing anything wrong.

This can show up in longer content production flows, in requirement specifications, or in complex coding tasks, where the AI gradually drifts away from the original constraints even though they were never explicitly changed.

The result is not random noise, but gradual deviation. The output becomes less consistent, less predictable, and harder to control as the project expands.

At this point, many misdiagnose the issue as a problem with the model itself. In reality, it is a problem of missing structure.

Creating a stable reference outside the context window

The most effective way to work with context windows is to move the most important decisions out of the conversation itself.

By establishing a small set of MD files that define the project’s fixed constraints, you create an external reference that can be reused across context windows and sessions.

These MD files function as a form of structural memory. They make it possible to work iteratively with AI without losing coherence, even as complexity increases.

How MD files are used in practice

A solid practice is to begin any AI-driven project by asking the model to propose an overall structure and to formalize the core constraints as one or more MD files.

These files are then provided as a constant reference whenever work continues. This ensures the model always operates from the same foundation, regardless of how much new information is introduced.

One rule matters above all: changes to MD files must never happen implicitly. The model should always propose modifications explicitly and with justification, so decisions remain deliberate and traceable.

This requires discipline, but it significantly improves stability.

Sharp content matters more than comprehensive content

An effective MD file should not attempt to capture everything.

It works best when it contains only what is essential to the project’s direction.

Typically, that includes:

• the project’s purpose and success criteria

• fixed decisions and underlying assumptions

• principles for structure, style, or architecture

• clear boundaries defining what must not change

MD files are not documentation in the traditional sense. They are governing principles designed to reduce ambiguity and enforce consistency.

Structure can be introduced retroactively

Even projects that have already become complex can be stabilized.

By asking the AI to analyze existing work and derive proposed MD files, it is often possible to quickly identify which decisions were never made explicit and where understanding began to drift.

This also serves as a valuable quality check. Vague or contradictory formulations in MD files often point directly to the moments where coherence started to break down.

A condition, not a trick

Context windows are not an advanced optimization technique. They are a fundamental constraint.

When processes are designed with this constraint in mind, working with AI becomes more stable, more controllable, and far less frustrating. This is often where the difference emerges between ad hoc experimentation and professional use.

Understanding context windows is not an enhancement.

It is a working condition.