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① 모드를 항상 'Thought' 또는 'Pro'로 전환 (개인적으로는 'Thought'가 더 좋다고 생각합니다)
② 키워드를 나열하는 대신 '문장으로 장면을 묘사'하세요.
③ 주제, 배경, 분위기를 세 가지 요소로 지정하세요.
④ 한 번에 완벽함을 추구하지 말고, '점진적인 수정'을 적용하세요.
⑤ 부정적인 표현을 피하고, 긍정적인 문장으로 지시하세요.
⑥ 품질 관련 키워드(4K, high resolution)는 영어로 추가하세요.
⑦ 목적을 처음에 명확히 명시하세요 (예: "{argument name="purpose" default="제안서용 다이어그램"}").
⑧ 불필요한 요소는 부정적인 프롬프트를 사용하여 제외하세요.
⑨ 복잡한 편집을 한 번에 시도하지 말고, 단계별로 진행하세요.
⑩ 자주 사용하는 프롬프트는 템플릿으로 저장하세요.
인포그래픽 / 교육용 시각 자료 - Nano Banana Pro의 안정적인 출력을 위한 프롬프트 전략
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이 문서는 Nano Banana Pro (Gemini 3 Pro Image)용 효과적인 프롬프트 작성을 위한 10가지 지침 및 전략을 종합적으로 다루며, 출력 안정화 및 깨진 텍스트 방지에 중점을 둡니다. '생각 모드(Thought Mode)' 또는 '프로 모드(Pro Mode)'로 전환하고, 키워드 목록 대신 설명적인 문장을 사용하며, 주제/배경/분위기를 지정하고, 품질을 위해 긍정적인 언어와 영어 키워드를 사용할 것을 강조합니다.
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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