Introduction

What is nano bannana?

nano bannana is an AI image generation service that turns text prompts or reference images into high quality visuals. It is designed for creators who need fast, consistent results with clear pricing and simple usage.

Who these docs are for

These docs are for marketers, designers, founders, and teams who want to use nano bannana as a practical AI image generator. If you need on brand visuals for ads, landing pages, product pages, or social posts, this documentation focuses on the workflow that gets you there. The emphasis is on repeatable results, clear prompts, and an efficient review loop, not on abstract theory or vague inspiration. Use this guide if you are deciding whether nano bannana fits your needs, or if you are already using the product and want a consistent process.

nano bannana in one sentence

nano bannana is an independent interface built on top of the Nano Banana API that helps teams generate on brand visuals with a clear prompt workflow and transparent credit costs.

Core concepts to understand

Before you generate your first image, align on a few concepts that affect quality and consistency.

Prompt: The text description that defines subject, style, mood, and constraints. In nano bannana, prompt clarity is the single biggest factor in consistent outputs.
Reference image: An optional image that guides composition, lighting, or style. Use this when brand consistency matters or when you are iterating on an existing concept.
Mode: Text to image is best for new ideas. Image to image is best for refining a concept while keeping a stable look.
Credits: Credits are the unit used to price generations. The generator shows cost before you run a job so you can plan output and budget.
History: nano bannana keeps prompt history attached to each result so teams can review, compare, and reuse what worked.

Quick start workflow

If you are new, follow this repeatable flow for your first session.

Define the goal: Identify where the image will be used. A hero image needs different framing than a social post.
Write a prompt: Keep it concise. State subject, style, mood, and constraints. Avoid long paragraphs.
Add references: If you need a consistent brand look, upload one or two reference images.
Select model and ratio: Pick a model that matches your style and a ratio that fits placement.
Generate, review, refine: Compare variations, edit the prompt, and run again until the result matches the brief.

Prompt structure that works

A prompt that includes the right parts gets you closer to the desired output with fewer retries.

Structure: Subject + Context + Style + Lighting + Color + Mood + Constraints

Example:

Minimalist product hero, studio lighting, soft shadows, neutral background, high contrast, clean composition, brand colors, space for headline

Using nano bannana as a shell product

nano bannana is a shell product that provides a dedicated workflow, credit visibility, and prompt history on top of the Nano Banana API. That means you get a stable interface for generation without having to build your own tooling. It also means nano bannana is an independent service and not affiliated with or endorsed by the upstream provider. This is important when you document your internal process or communicate usage rights to your team.

Best practices for consistent results

Write for outcomes: Describe how the image will be used. Include placement or channel.
Keep constraints stable: When testing variations, change one variable at a time.
Reuse winning prompts: Save prompt recipes that produce reliable results and apply them to new campaigns.
Document references: Track which reference images create the desired look so the team can reuse them.
Build a small prompt library: A shared library reduces guesswork and speeds up onboarding.

Common mistakes to avoid

Overloading prompts with too many unrelated details.
Ignoring aspect ratio, which causes awkward framing and wasted rework.
Skipping references when brand alignment is required.
Not reviewing history, which makes it harder to repeat a successful look.

How to use these docs

These docs are designed to match the way teams actually work. Start with the basics, then go deeper based on your role.

New users: Read the quick start and prompting basics to learn the structure.
Marketing leads: Review credits and pricing so you can plan campaigns.
Designers: Focus on references, aspect ratios, and prompt iteration.
Teams: Use the prompt library guidance to build shared templates.

The goal is not to memorize theory. The goal is to create a reliable workflow that you can repeat across campaigns.

Quality checklist for outputs

Before you export, check the result against a few practical criteria:

Composition: Is the subject clearly framed for the placement?
Brand fit: Do colors, lighting, and style match your brand system?
Readability: Is there enough negative space for text or UI?
Consistency: Does this match other assets in the campaign?
Purpose: Does it fit the channel it was designed for?

If the answer is no, refine one variable and generate again.

Glossary

Brief: A short description of the asset goal and placement.
Placement: The channel or surface where the image will be used.
Reference: An image that guides layout, lighting, or style.
Variation: A controlled change in a single prompt variable.
History: The record of prompts and outputs used to repeat success.

Documentation map

Use this map to find the most relevant sections quickly:

Start here: Prompting basics and the feature overview.
Planning usage: Credits and pricing, plus any billing questions.
Team workflows: Prompt libraries and review checklists.
Comparisons: Alternative pages that explain how nano bannana fits in the market.

If you are evaluating tools, start with the AI image generator page, then read the prompting basics and credits guides.

Can I share prompts across a team?
Yes. A shared prompt library helps teams create consistent visuals and reduces rework.