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① モードは必ず「Thought」または「Pro」に切り替える（「Thought」の方が良いと感じる）
② キーワードを羅列するのではなく、「文章で情景を描写する」
③ 被写体、背景、雰囲気を三点セットで指定する
④ 一発で完璧を目指さず、「段階的な修正」を行う
⑤ 否定的な表現を避け、肯定的な文章で指示を出す
⑥ 品質に関するキーワード（4K、high resolution）は英語で追加する
⑦ 冒頭で目的を明確にする（例：`{argument name="purpose" default="提案書用の図"}`）
⑧ ネガティブプロンプトを使って不要な要素を除外する
⑨ 複雑な編集を一度に試みず、セグメントに分けて進める
⑩ よく使うプロンプトはテンプレートとして保存する
インフォグラフィック / 教育ビジュアル - Nano Banana Pro プロンプト戦略:安定した出力を実現するために
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これは、Nano Banana Pro (Gemini 3 Pro Image) 用の効果的なプロンプトを作成するための、包括的な 10 の指示と戦略のセットです。出力の安定化と文字化けの回避を目的としています。「思考モード」または「プロモード」への切り替え、キーワードリストではなく説明的な文章の使用、被写体/背景/雰囲気の指定、品質向上のための肯定的な言葉と英語キーワードの使用を強調しています。
This tweet outlines an advanced, two-step prompting strategy to significantly improve the quality of AI output, specifically for generating YouTube thumbnails using Nano Banana Pro. The strategy involves first asking a powerful LLM (like GPT-5.2) to systematically summarize the best practices, terminology, and constraints for creating Nano Banana Pro prompts (creating a 'map'). Then, the user asks the LLM to generate the final, specific prompt based on that established system and the user's requirements, resulting in much more consistent and targeted output.
This tweet outlines an advanced, two-step prompting strategy to significantly improve the quality of AI output, specifically for generating YouTube thumbnails using Nano Banana Pro. The strategy involves first asking a powerful LLM (like GPT-5.2) to systematically summarize the best practices, terminology, and constraints for creating Nano Banana Pro prompts (creating a 'map'). Then, the user asks the LLM to generate the final, specific prompt based on that established system and the user's requirements, resulting in much more consistent and targeted output.
A guide on how to structure prompts for Nano Banana Pro to achieve better results, emphasizing the use of specific roles, concrete descriptions (e.g., lighting), and structured formatting like bullet points or mini sections.
A template for 'Forbidden Items' (negative prompts) specifically tailored for generating manga panels in NanoBanana Pro, ensuring correct reading order, language, and format.
A meta-prompting process description (step 4) that instructs the AI to reverse-engineer a reusable prompt template based on a clearly defined visual goal. This template must support custom variables for long-term use in advertising creation.
A detailed prompt designed to generate a highly realistic, documentary-style photograph of two celebrities (Pedro Pascal and Sydney Sweeney) figure skating at the Winter Olympics. It uses extensive negative prompting and technical cues (iPhone photography realism, natural arena lighting, visible pores, film grain) to avoid a polished, cinematic, or AI-generated look.
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Data collected and processed: IP addresses, Device identifiers, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Users’ profiles, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Probabilistic identifiers, Browsing and interaction data, Non-precise location data
Data collected and processed: IP addresses, Device characteristics, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Authentication-derived identifiers, Browsing and interaction data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Authentication-derived identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, User-provided data, Non-precise location data, Precise location data
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Authentication-derived identifiers, Browsing and interaction data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Users’ profiles, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, User-provided data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Users’ profiles, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device characteristics, Device identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
Data collected and processed: IP addresses, Device identifiers, Probabilistic identifiers, Browsing and interaction data, Non-precise location data, Privacy choices
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