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  "path": "/t/structured-prompt-framework-for-multi-domain-workflows-reducing-cognitive-load/1377227#post_1",
  "publishedAt": "2026-03-19T16:28:32.000Z",
  "site": "https://community.openai.com",
  "textContent": "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).\n\nThe core idea is to enforce a consistent interaction pattern rather than treating each prompt independently.\n\nExample structure I’m using:\n\n→ Input → Interpretation → Constraint → Output\n\nWhere: • Input = raw user context • Interpretation = model reframes the task clearly • Constraint = limits scope / format to reduce overload • Output = structured, actionable response\n\nWhat 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\n\nI’ve seen a few discussions around reusable prompt patterns, but I haven’t seen much around multi-domain workflows or cognitive load specifically.\n\nWhere 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?\n\nHappy to share more concrete examples if useful.",
  "title": "Structured prompt framework for multi-domain workflows (reducing cognitive load)"
}