Structured prompt framework for multi-domain workflows (reducing cognitive load)

I’ve been experimenting with a structured prompting approach to make LLM outputs more usable across different types of tasks (planning, decision-making, creative work, etc). The core idea is to enforce a consistent interaction pattern rather than treating each prompt independently. Example structure I’m using: → Input → Interpretation → Constraint → Output Where: • Input = raw user context • Interpretation = model reframes the task clearly • Constraint = limits scope / format to reduce overload • Output = structured, actionable response What I’m seeing: • outputs are more consistent across domains • less “over-helpful” or overly verbose responses • easier to reuse patterns instead of rewriting prompts each time I’ve seen a few discussions around reusable prompt patterns, but I haven’t seen much around multi-domain workflows or cognitive load specifically. Where I’m curious: • has anyone tried similar structured prompting loops? • what constraints have you found most effective for keeping outputs usable? • how do you prevent models from drifting into over-complex responses? Happy to share more concrete examples if useful.