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  "path": "/t/structured-prompt-framework-for-multi-domain-workflows-reducing-cognitive-load/1377227#post_3",
  "publishedAt": "2026-03-21T16:10:07.000Z",
  "site": "https://community.openai.com",
  "textContent": "yeah, this makes sense.\n\nimo most ppl overfocus on tone / wording / “make it sound human” stuff, but the real win is structure. if the model knows what came in, what the task actually is, what box it has to stay in, and what kind of thing it needs to spit back, the output gets way more solid.\n\nthat’s prob why your setup works across diff domains. less drift, less yap, less of the model trying to be “helpful” and just going off-road.\n\ni’d prob add one more step tho:\n\ninput > interpretation > constraint > validation > output\n\nthat validation part is lowkey the big one. not just format check, more like: did it actually get the ask right, stay in bounds, and return smth usable w/o extra cleanup?\n\nbc that’s where a lot of LLM weirdness slips in. output can look clean as hell and still be wrong bc the model misread the task up front.\n\nso yeah, agree on the cognitive load point. in real workflows, structure usually matters more than phrasing. tone helps, sure, but structure is what stops the model from freestyling.\n\nat that point it’s not even just prompt engineering anymore, it’s basically lightweight workflow design for LLMs.",
  "title": "Structured prompt framework for multi-domain workflows (reducing cognitive load)"
}