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① Alwaysswitch the mode to 'Thought' or 'Pro' (I feel 'Thought' is better)
② 'Describe the scene in sentences' rather than listing keywords
③ Specify the subject, background, and atmosphere as a set of three points
④ Don't aim for perfection in one go; apply 'gradual corrections'
⑤ Avoid negative expressions; give instructions in positive sentences
⑥ Add quality keywords (4K, high resolution) in English
⑦ Clearly state the purpose at the beginning (e.g., "{argument name="purpose" default="Diagram for a proposal"}")
⑧ Exclude unnecessary elements using negative prompts
⑨ Do not attempt complex edits all at once; proceed in segments
⑩ Save frequently used prompts as templates
Infographic / Edu Visual - Nano Banana Pro Prompting Strategy for Stable Output
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This is a comprehensive set of 10 instructions and strategies for writing effective prompts for Nano Banana Pro (Gemini 3 Pro Image), aimed at stabilizing output and avoiding garbled text. It emphasizes switching to 'Thought Mode' or 'Pro Mode', using descriptive sentences instead of keyword lists, specifying subject/background/atmosphere, and using positive language and English keywords for quality.
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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