Nano Banana Prompting Guide: How to Get the Best Results (2026)

The difference between a mediocre AI image and a production-ready one usually comes down to how you write the prompt. Nano Banana models built on Google’s Gemini architecture understand natural language far better than keyword lists, which means the old “tag soup” approach of stacking comma-separated terms produces worse results than writing a clear, descriptive sentence. This guide covers the prompting frameworks, templates, and model-specific techniques that produce consistently strong output from both Nano Banana 2 and Nano Banana Pro.

The core formula is straightforward: Subject + Action + Context + Style. Everything in this guide builds on that foundation, from basic text-to-image generation through advanced techniques like character consistency, text rendering in 10 languages, and search-grounded real-world accuracy.

The Golden Rules of Nano Banana Prompting

Before diving into specific techniques, four principles apply to every prompt you write for any Nano Banana model. These rules come from extensive testing by Google’s own prompting team and consistently produce better results than any single technique in isolation.

Use natural language, not tag soups. Nano Banana Pro applies deep reasoning before generating, which means it interprets grammatical structure, implied relationships, and contextual nuance. A prompt like “cool car, neon, city, night, 8k” forces the model to guess at the relationships between those fragments. “A cinematic wide shot of a futuristic sports car speeding through a rainy Tokyo street at night, with neon signs reflecting off the wet pavement and the car’s metallic chassis” gives the model a complete scene to reason about.

Be specific about subject, setting, lighting, and mood. Vague subjects produce generic output. Instead of “a woman,” write “a sophisticated elderly woman wearing a vintage Chanel-style suit.” Describe textures explicitly: “matte finish,” “brushed steel,” “soft velvet,” “crumpled paper.” The more precise your description, the less the model has to invent on its own.

Edit, don’t re-roll. If an image is 80% correct, do not generate a new one from scratch. The model handles conversational edits exceptionally well. Simply say: “That’s great, but change the lighting to sunset and make the text neon blue.” This preserves the elements you already like while fixing what you do not.

Provide context with the “why” or “for whom.” Because Nano Banana Pro reasons about your intent, context changes its artistic decisions. “Create an image of a sandwich for a Brazilian high-end gourmet cookbook” produces dramatically different results than “create an image of a sandwich” — the model infers professional plating, shallow depth of field, and studio lighting from the context alone.

The Prompt Formula: Subject + Action + Context + Style

Every effective Nano Banana prompt contains these components in order of priority. Elements mentioned earlier in the prompt carry more weight in the final image, so list what matters most first.

You do not need all five components in every prompt, but including at least Subject + Context + Style produces significantly more consistent results than a subject alone. For complex scenes, add Action and Composition to give the model explicit spatial instructions.

A practical example applying the full formula: “A weathered fisherman mending a net on the bow of a wooden boat at dawn. Misty harbor, warm golden light cutting through fog. Documentary photography style, 35mm film, shallow depth of field, eye-level shot.” Every element tells the model something specific, leaving almost nothing to chance.

Image Editing: Remove, Add, Replace, Restyle

Nano Banana 2 excels at image editing because it reads existing pixels and predicts how they should change to match your instructions. No manual masking is required — the model performs semantic masking automatically, identifying target objects from your description.

Object Removal

The removal template keeps edits clean and prevents unintended changes to surrounding elements. Start with the verb “remove” and name the specific element.

Template: “Using this image, remove the [specific element]. Keep everything else in the image exactly the same, preserving the original style, lighting, and composition.”

If the model modifies areas you want preserved, add explicit preservation instructions: “Do not change the background, lighting, or any other objects.” Nano Banana 2’s conversational architecture means you can iterate: “Good, but the shadow from the removed object is still visible. Remove that shadow as well.”

Object Addition

When adding elements, define both what the new object looks like and where it should appear in the scene. Describing how the object interacts with existing lighting prevents the common problem of “pasted-on” additions that look out of place.

Template: “Using this image, add a [specific element] to the [location]. Ensure the new object matches the lighting and perspective of the original image.”

Object Replacement

Replacement combines removal and addition in a single step. The key instruction is keeping everything else unchanged.

Template: “Using this image, replace the [old element] with a [new element]. Keep everything else in the image exactly the same, preserving the original style, lighting, and composition.”

Style Transfer

Nano Banana can repaint an entire image in a different artistic style while preserving layout and subject positions. This works because the model understands scene structure separately from visual rendering.

Template: “Change this image to [specific style]. Ensure the composition and the position of all objects remain exactly the same as the original.”

Example styles that produce strong results: “sleek minimalist futurism with smooth white polymer and chrome,” “oil painting in the style of the Dutch Masters,” “retro 1970s magazine illustration with halftone dots,” “low-poly 3D render with flat shading.”

Text Rendering and Infographics

Accurate text in AI-generated images has historically been one of the hardest problems in the field. Nano Banana Pro solves it with 94% text rendering accuracy across 10 languages, but getting clean results requires specific prompting techniques.

Put the exact text you want in quotation marks within your prompt. The model treats quoted strings as literal text to render, not as descriptive context. Specify the text style — “editorial headline,” “technical label,” “hand-drawn whiteboard” — to control how the text appears.

Nano Banana Pro can also compress complex information into visual formats. You can input PDFs, data tables, or long text passages and ask the model to generate infographics, diagrams, or visual summaries. For best results, specify the sections and labels you want: “Generate a clean, modern infographic summarizing [topic]. Include charts for [metrics] and highlight [key data] in a stylized pull-quote box.”

Stop using ‘tag soups’ and start acting like a Creative Director. Nano Banana Pro doesn’t just match keywords; it understands intent, physics, and composition.

Google AI for Developers

Character Consistency Across Multiple Images

Maintaining a recognizable character across different scenes, poses, and lighting conditions is one of the most difficult tasks in AI image generation. Repeating text descriptions produces a slightly different person each time. The character sheet method solves this.

Step 1: Generate a 360-degree character sheet showing your character from front, three-quarter, side, and back views against a neutral background. Use a prompt like: “Create a character reference sheet of [detailed character description]. Show the character from front, three-quarter left, side, and back views. Neutral white background, even lighting, full body. Label each view.”

Step 2: Use that character sheet as a reference image in all subsequent generations. Attach the image and write: “Using the character from the reference image, show them [new action] in [new setting]. Maintain their exact appearance, clothing, and proportions.”

Nano Banana Pro can maintain identity across up to 5 characters and track the fidelity of up to 14 reference objects in a single workflow. This makes it practical for storyboarding, comic creation, product catalog photography, and any project where visual consistency matters across dozens of output images.

Nano Banana 2 vs Nano Banana Pro: Prompting Differences

The two models have fundamentally different architectures, which means the same prompt can produce different quality depending on which model processes it. Choosing the right model for each task matters as much as writing the prompt itself.

Nano Banana 2 (built on Gemini 2.5 Flash Image) is a rapid pattern-matching engine. It reads existing pixels, predicts changes, and generates output fast — typically 3 to 5 seconds. It thrives on conversational, iterative prompts where you refine results step by step. It also uses Google Search for real-time data grounding, which improves accuracy for prompts referencing specific real-world subjects. Nano Banana 2 supports a 131,072 input token context window and offers extra aspect ratios including 1:4, 4:1, 1:8, and 8:1.

Nano Banana Pro (built on Gemini 3 Pro Image) is a reasoning engine that plans scene logic before rendering. It excels at structured, detailed briefs where you describe the complete scene upfront. Text rendering, complex multi-object compositions, and infographic generation all perform better on Pro. It supports a 65,536 input token window with 32,768 output tokens. Generation takes 5 to 15 seconds.

The practical rule: use Nano Banana 2 for editing, style transfer, and rapid iteration. Use Nano Banana Pro for final production renders, text-heavy images, and complex scenes built from scratch.

Advanced Techniques

Three capabilities push Nano Banana beyond basic image generation into production-grade creative tooling.

Search-grounded prompts leverage Google Search to verify visual accuracy for real-world subjects. When your prompt references a specific building, landmark, biological specimen, or historical figure, the model retrieves reference data to inform its output. This reduces the hallucinated details that plague isolated models. The feature works automatically — you simply mention real-world subjects and the model decides when external grounding will improve accuracy.

Aspect ratio control gives you precise output dimensions for any platform. Both models support 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, and 21:9. Nano Banana 2 adds extreme ratios like 1:4 and 8:1 for banners and vertical scrolling content. Specify the ratio in your prompt or set it as an API parameter.

Thinking levels on Nano Banana 2 control how much the model reasons before generating. Set to Minimal for fast exploration when you are brainstorming dozens of variations. Switch to High or Dynamic for complex multi-object scenes where spatial relationships and lighting interactions matter. The time difference is small, but High mode produces more coherent compositions for challenging prompts.

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