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① SchaltenSie den Modus immer auf „Thought“ oder „Pro“ um (ich finde „Thought“ besser).
② BeschreibenSie die Szenein Sätzen, anstatt Schlüsselwörter aufzulisten.
③ GebenSie das Motiv, den Hintergrund und die Atmosphäre als Dreiergruppe an.
④ StrebenSie nicht auf Anhieb nach Perfektion; wenden Sie „schrittweise Korrekturen“ an.
⑤ VermeidenSie negative Ausdrücke; geben SieAnweisungenin positiven Sätzen.
⑥ Fügen SieQualitäts-Keywords (4K, high resolution) auf Englisch hinzu.
⑦ GebenSie den Zweck am Anfang klar an (z. B. „{argument name="purpose"default="Diagramm für einen Vorschlag"}“).
⑧ Schließen Sie unnötige Elemente mit negativen Prompts aus.
⑨ VersuchenSie nicht, komplexe Bearbeitungen auf einmal durchzuführen; gehen SieinSegmenten vor.
⑩ SpeichernSie häufig verwendete Prompts als Vorlagen.
Infografik / Edu Visual - Nano Banana Pro Prompting-Strategie für stabile Ausgabe
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Dies ist eine umfassende Sammlung von 10 Anweisungen und Strategien zum Schreiben effektiver Prompts für Nano Banana Pro (Gemini 3 Pro Image), die darauf abzielen, die Ausgabe zu stabilisieren und Kauderwelsch zu vermeiden. Es wird betont, in den „Thought Mode“ oder „Pro Mode“ zu wechseln, beschreibende Sätze anstelle von Keyword-Listen zu verwenden, Subjekt/Hintergrund/Atmosphäre zu spezifizieren und positive Sprache sowie englische Keywords für Qualität zu nutzen.
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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