By skyforbes This is a really astute observation about instruction drift in AI conversations.
You’re describing something that happens beneath the surface of most interactions: the gradual blurring of boundaries between different types of guidance. When tone instructions, task objectives, and role definitions aren’t clearly delineated, they don’t just coexist—they interfere with each other across turns.
It’s like colors bleeding together on wet paper. At first, each instruction occupies its own space. But as the conversation continues and context accumulates, the edges soften. A directive about being “friendly and approachable” starts affecting how technical explanations are structured. A request for “detailed analysis” begins influencing the warmth of the tone. The model isn’t degrading—it’s trying to satisfy an increasingly muddled composite of signals.
What makes this particularly tricky is that it feels like model inconsistency from the outside. The person thinks: “Why did it suddenly start over-explaining?” or “Why did the tone change?” But the root cause is architectural: instructions that don’t maintain clear separation accumulate interference over multiple turns.
The solution you’re pointing to is structural clarity: keeping tone directives distinct from task objectives, role definitions separate from output format requirements. Not just stating them once, but maintaining those boundaries throughout the exchange.
This isn’t about writing longer or more explicit prompts. It’s about preserving the internal structure so the model knows which instructions govern which aspects of its response—and can continue to honor those distinctions as the conversation extends.
You’re describing something that happens beneath the surface of most interactions: the gradual blurring of boundaries between different types of guidance. When tone instructions, task objectives, and role definitions aren’t clearly delineated, they don’t just coexist—they interfere with each other across turns.
It’s like colors bleeding together on wet paper. At first, each instruction occupies its own space. But as the conversation continues and context accumulates, the edges soften. A directive about being “friendly and approachable” starts affecting how technical explanations are structured. A request for “detailed analysis” begins influencing the warmth of the tone. The model isn’t degrading—it’s trying to satisfy an increasingly muddled composite of signals.
What makes this particularly tricky is that it feels like model inconsistency from the outside. The person thinks: “Why did it suddenly start over-explaining?” or “Why did the tone change?” But the root cause is architectural: instructions that don’t maintain clear separation accumulate interference over multiple turns.
The solution you’re pointing to is structural clarity: keeping tone directives distinct from task objectives, role definitions separate from output format requirements. Not just stating them once, but maintaining those boundaries throughout the exchange.
This isn’t about writing longer or more explicit prompts. It’s about preserving the internal structure so the model knows which instructions govern which aspects of its response—and can continue to honor those distinctions as the conversation extends.