a fix that personally works for me about the ai productivity systems

ive been building and testing ai workflows for clients for months now and one thing keeps showing up over and over: most ppl dont have a workflow problem, they have a prompt structure problem. they dump everything into one giant message, hope the model magically understands priorities, and then wonder why the output feels messy or inconsistent.

the smallest fix that consistently works for me is breaking instructions into 3 layers instead of one:

1. the stable layer
   this is your non-negotiable rules: tone, constraints, goals, boundaries, formatting. never changes. never gets rewritten. the model only reads from it.

2. the task layer
   this is what changes per project: deliverables, steps, deadlines, scope. this updates without touching layer 1.

3. the runtime layer
   this is the live conversation: clarifications, edits, iterations. all the noise stays here instead of polluting the core logic.

i think once u separate these three, ai stops drifting and starts behaving like an actual assistant instead of a random text generator. ive seen this save hours every week for ppl who do writing, research, outreach, planning, or content operations. i first saw a version of this in one of the god of prompt frameworks and it kinda clicked why teams struggle: they try to scale ai without scaling structure. curious if anyone has their own architecture for keeping ai consistent across long professional workflows.