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Japanese AI Model Prescription Sheet

A photorealistic overhead shot of a Japanese prescription-style parody document on a warm wooden tabletop. The page is filled with Japanese text, ruled form fields, and a red seal stamp, creating a humorous fake medical form look.

Model: gpt-image-2Category: Meme/FunStyle: PhotographyLanguage: en

Prompt

{   "type": "Japanese prescription document parody",   "style": "photorealistic overhead document photo",   "subject": "a single white prescription sheet printed in Japanese, placed on a warm-toned wooden tabletop",   "document": {     "paper": {       "size": "A4 portrait",       "color": "white",       "orientation": "slightly rotated clockwise",       "condition": "clean flat sheet with subtle natural paper texture"     },     "print": {       "ink": "black with one red seal box",       "font": "Japanese Mincho-style serif mixed with simple sans-serif table text",       "linework": "thin gray-black ruled boxes and dividers"     },     "header": {       "title": "処方箋",       "subtitle": "(この処方箋は、大規模言語モデル用です)",       "date_label": "発行日:",       "date_value": "{argument name=\"issue date\" default=\"2025年6月5日\"}"     },     "top_fields": {       "count": 6,       "items": [         { "label": "患者名", "value": "{argument name=\"patient name\" default=\"ChatGPT\"}" },         { "label": "性別", "value": "N/A" },         { "label": "年齢", "value": "N/A" },         { "label": "モデル名", "value": "{argument name=\"model name\" default=\"GPT-5.4 Thinking\"}" }       ]     },     "main_section": {       "left_title": "持病・症状(大規模言語モデル特有の持病)",       "left_count": 8,       "left_items": [         "1.幻覚(Hallucination)傾向",         "2.知識のカットオフによる情報の陳腐化",         "3.バイアス(学習データ由来の偏見)",         "4.プロンプトに対する過剰適応(迎合傾向)",         "5.長文での一貫性の低下",         "6.複雑な推論時の論理飛躍",         "7.不確実性の過小評価(自信過剰)",         "8.計算資源使用による応答速度の低下傾向"       ],       "right_title": "処方内容(治療・対策)",       "right_count": 8,       "right_items": [         "・事実確認の強化(外部ツール併用)",         "・定期的な知識更新とファインチューニング",         "・バイアス検出と軽減の継続的モニタリング",         "・システムプロンプトによる適切な制御",         "・出力の構造化と分割生成の推奨",         "・思考過程の明示と検証ステップの導入",         "・不確実性の明示と根拠提示の徹底",         "・リソース最適化と応答効率の改善"       ]     },     "notes": {       "label": "備考",       "text": "継続的な評価とフィードバックにより、モデルの健全性を維持すること。"     },     "footer_fields": {       "count": 4,       "items": [         { "label": "医師名", "value": "{argument name=\"doctor name\" default=\"サム・アルトマン\"}" },         { "label": "医療機関名", "value": "{argument name=\"institution name\" default=\"OpenAI\"}" }       ]     },     "seal": {       "position": "bottom right inside the form",       "shape": "rounded rectangular red stamp box",       "text_lines": 3,       "text": ["OpenAI", "代表取締役"...

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