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  "path": "/t/structured-prompt-framework-for-multi-domain-workflows-reducing-cognitive-load/1377227#post_5",
  "publishedAt": "2026-03-24T02:09:14.000Z",
  "site": "https://community.openai.com",
  "textContent": "Really like this framing — especially the distinction between structure and phrasing.\n\nI’ve been using something similar, but more as a small pipeline:\n\n**Input → Interpretation → Constraint → Reduction → Output → (Optional Validation)**\n\n  * **Interpretation** clarifies the task before generation.\n\n  * **Constraint** defines format, scope, and what “done” looks like.\n\n  * **Reduction** pushes toward the minimum viable useful result instead of maximum completeness.\n\n  * **Validation** is helpful for higher-stakes cases like math, billing, or compliance.\n\n\n\n\nThe part that feels most important in practice is defining **done** inside the constraint — e.g. usable without editing, fits on one screen, directly actionable.\n\nYour clinic example shows this well: messy input in, multiple usable artifacts out, no reformatting.\n\nThat feels less like prompting and more like building a reliable transformation layer.\n\nCurious how you’re handling incomplete or ambiguous inputs — seems like that’s where the interpretation step becomes the main control point.",
  "title": "Structured prompt framework for multi-domain workflows (reducing cognitive load)"
}