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① Toujours passer en mode « Thought » ou « Pro » (je trouve que « Thought » est mieux)
② Décrire la scène par des phrases plutôt que par une liste de mots-clés
③ Spécifier le sujet, l'arrière-plan et l'atmosphère comme un ensemble de trois points
④ Ne pas viser la perfection du premier coup ; appliquer des « corrections graduelles »
⑤ Éviter les expressions négatives ; donner des instructions dans des phrases positives
⑥ Ajouter des mots-clés de qualité (4K, haute résolution) en anglais
⑦ Indiquer clairement l'objectif au début (par exemple, « {argument name="purpose" default="Diagramme pour une proposition"} »)
⑧ Exclure les éléments inutiles à l'aide de prompts négatifs
⑨ Ne pas tenter des modifications complexes en une seule fois ; procéder par segments
⑩ Enregistrer les prompts fréquemment utilisés comme modèles
Infographie / Visuel éducatif - Stratégie de Prompting Nano Banana Pro pour une Sortie Stable
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Voici un ensemble complet de 10 instructions et stratégies pour rédiger des invites efficaces pour Nano Banana Pro (Gemini 3 Pro Image), visant à stabiliser la sortie et à éviter le texte brouillé. Il met l'accent sur le passage au « Mode Pensée » ou au « Mode Pro », l'utilisation de phrases descriptives au lieu de listes de mots-clés, la spécification du sujet/arrière-plan/atmosphère, et l'utilisation d'un langage positif et de mots-clés anglais pour la qualité.
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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