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Maximize output quality and efficiency when using Nano Banana for image generation and editing

Developers, marketers, and creators building products with AI image generation

Immediate implementation

What Success Looks Like

Consistent, high-quality image outputs that match user intentions with minimal iteration cycles

Steps to Execute

Use precise, single-turn edit commands rather than complex multi-step instructions

Leverage the model's world knowledge by being specific about context and style

Structure multi-edit workflows as sequential single turns to maintain quality

Test prompts iteratively since image quality doesn't degrade with multiple turns

Provide clear, unambiguous instructions like working with a creative contractor

Checklist

Is your prompt specific about what you want changed?

Are you requesting one clear edit at a time?

Have you provided sufficient context for the model to understand your intent?

Are you leveraging the model's knowledge of styles, formats, and contexts?

Inputs Needed

Clear description of desired output
Base image (if editing)
Specific style or context requirements

Outputs

High-quality generated or edited images
Consistent results across iterations
Reduced prompt engineering time

Example

“Instead of 'make this image better and add text and change colors', use 'add the text Pixel - the phone for AI nerds under the phone image', then in next turn 'change the background to a luxury magazine style'”