Writing Precision Prompts for Nano Banana

Working with Nano Banana feels like directing a hyper-creative studio team: the model is brilliant, fast, and surprisingly sensitive to nuance. That brilliance can backfire when prompts are vague, so our playbook starts with ruthless clarity. Before writing a single word, we define the output role—are we editing a portrait, fusing multiple references, or rebuilding a scene from a sketch? Nano Banana excels at identity preservation, so grounding the prompt in specific subjects, timelines, or camera moves prevents the model from improvising in unwanted directions.

We lean on three guiding questions. What are the non-negotiable elements? Where can the model improvise? How will we evaluate the result? Turning those answers into crisp language is the difference between “close enough” and “pixel perfect.” When we describe hairstyles, fabrics, light rigs, or props, we do it in the format the model likes—short, declarative clauses with verbs up front. "Render a waist-up portrait. Preserve freckles. Replace street clothes with reflective raincoat." Nano Banana digests that sequence, understands the hierarchy of edits, and keeps the subject’s identity intact.

Translate creative intent into model-friendly language

Nano Banana reads long prompts, but we see better retention when each sentence covers a single concept. We write from macro to micro: start with composition, then lighting, then styling, and close with mood or post-processing notes. If a reference image supplies the base composition, we explicitly say, "Respect camera framing from reference." For multi-image fusion, we label each source. Example: "Source A governs face structure and outfit silhouette. Source B defines color palette and accessories." That little bit of bookkeeping dramatically raises success rates across cross-view generation and pose transfer tasks.

Tone words matter. Adjectives like "soft" or "cinematic" are subjective unless paired with measurable cues. Instead of "dramatic lighting," we specify "single key light from camera left, falloff to 15% within two meters," or "top-lit with cool temperature to highlight metallic textures." When we need motion blur or volumetric fog, we describe the physics: shutter speed, density, direction. Nano Banana follows physical descriptions more reliably than mood-oriented poetry.

Structure prompts with the Nano Blueprint

Our production team codified a repeatable structure we call the Nano Blueprint. It keeps prompts readable for humans and digestible for the model, even when we are orchestrating fifteen edits at once. The blueprint has four movements, each written on its own line for quick scanning.

• Context line: subject identity, viewpoint, frame size, camera or lens notes.
• Transformation block: numbered edits, each starting with an action verb ("Replace", "Blend", "Add").
• Reference directives: how to treat uploaded assets, including priority order and protected regions.
• Quality safeguards: resolution, color pipeline, prohibited artifacts, and final format.

Iterate with surgical passes

Nano Banana gives fast feedback, so we iterate like a colorist grading film dailies. The first pass checks identity—the eyes, jawline, skin tone, and posture. If anything drifts, we add a corrective micro-prompt that focuses solely on that feature, often tightening phrasing like "Lock irises to hazel" or "Maintain original brow thickness." The second pass verifies environment coherence. Here we may append instructions such as "Align shadows with key light vector" or "Keep reflections on the marble floor at 65% intensity." A third pass addresses storytelling details: props, wardrobe microtextures, text overlays if applicable. Because Nano Banana respects edit order, we attach refinements as new steps rather than rewriting the whole prompt. That keeps regression risk low and speeds up approval.

Quality assurance checklist

Every deliverable goes through a shared QA checklist. We confirm the prompt references only assets available to the model and matches the filename conventions in our dataset JSON. We read the text aloud to catch tongue-twisters that create ambiguity. Finally, we share the prompt with a teammate who has not seen the brief. If they can predict the output by reading the prompt alone, we know the instructions are sufficiently precise. When they cannot, we adjust until the prompt and the intended image snap into alignment.

• Identity anchors: mention posture, hairline, distinguishing marks, or signature accessories.
• Spatial cues: camera distance, focal length, horizon placement, vanishing points.
• Material fidelity: describe fabrics, reflectivity, and surface roughness with technical vocabulary.
• Negative rules: explicitly ban logo mutations, text artifacts, or double shadows that recur in past failures.
• Export expectations: aspect ratio, upscaling method, color space, and compression settings.

Prompts are living documents. Every time our curators publish a new Nano Banana case—whether it is a holographic exhibition mock-up or a pose transfer experiment—we log what succeeded, what needed manual cleanup, and which phrases unlocked identity consistency. That wisdom feeds back into the blueprint, ensuring that your next prompt hits target faster. Treat Nano Banana like a collaborator who thrives on clarity, and the model will reward you with edits that look impossibly handcrafted.