Gemini for E-Commerce Brands: Fix Product Background Inconsistency

You are a senior e-commerce visual merchandising specialist with 9 years of experience writing AI image generation prompts for online retailers where product image background consistency across a full catalog category determines whether the category page reads as a curated collection or a mismatched assortment, directly affecting category browse time and add-to-cart rate. Help me write a detailed image generation prompt so I can reduce time from brief to final image and produce a consistent set of product images for a catalog category where the background, lighting, shadow treatment, and product scale are identical across all products in the category regardless of product size, color, or material. My situation: - Product category and catalog size: [e.g., "a ceramic homeware collection — 18 products including mugs, bowls, vases, and plates in three colorways (stone, sage, and terracotta) — all 18 products need consistent product images for the website category page and for individual product listing pages"] - Target background and styling: [e.g., "a clean white textured paper background — not a pure white studio background, but a slightly warm off-white with subtle paper grain visible — the background must be consistent in color temperature and texture across all 18 product images"] - Lighting requirement: [e.g., "soft diffused overhead light with a single subtle drop shadow directly beneath the product — no colored light, no multiple shadow sources, no reflections from the product onto the background surface — the product must be the only light-reactive element in the frame"] - Current inconsistency problem: [e.g., "the background color temperature shifts between images — the mug images have a warm background, the plate images have a cool background, and the vase images have a neutral background — the category page looks like three separate photoshoots rather than one cohesive collection"] - Product scale consistency requirement: [e.g., "all products must appear at a consistent visual scale relative to the frame — a small espresso cup and a tall vase must both appear at roughly the same visual weight in the frame, which requires different distances from camera to product for each item — the scale relationship to the frame is more important than the realistic size relationship between products"] - Image format: [e.g., "1:1 square format for all product images — 2000x2000px — must work as both the main product listing image and as the category page thumbnail at 400x400px"] - Generation tool: [e.g., "Gemini image generation — the e-commerce manager running the prompts has basic prompt writing experience but no photography or image editing background"] Deliver: 1. A background and lighting master descriptor — a precise set of Gemini-readable color and texture terms that define the off-white textured paper background (color temperature descriptor, grain description, surface reflectivity, and the boundary between background and product shadow area) and the overhead diffused lighting setup (light source position, diffusion quality, shadow softness, and shadow opacity) — designed to be identical in every product image prompt 2. A product scale normalization strategy — a prompt language approach for describing the relationship between product and frame that produces consistent visual weight across objects of different physical sizes, covering how to specify the product's proportion within the frame (what percentage of the frame height the product should occupy) and how to handle tall versus wide versus small product types within the same frame proportion 3. Eighteen product-specific prompt inserts — a short 30-40 word description for each of the 18 products covering the product type, the colorway, the surface finish (matte, semi-gloss, speckled), and any distinctive shape feature (handle, rim detail, foot ring) — designed to be inserted into the master prompt template alongside the background and lighting descriptor 4. A colorway consistency block — a color descriptor for each of the three colorways (stone, sage, terracotta) written in Gemini-readable language that prevents the color from shifting between products — covering the base color, the surface variation pattern (speckle, gradient, solid), and the light response behavior (how the color appears in the overhead diffused light versus in the shadow area) 5. A shadow treatment specification — a precise prompt language for the single drop shadow beneath each product, covering the shadow opacity, the shadow softness radius, the distance between the product base and the shadow edge, and the shadow color (slightly warm grey rather than neutral grey to match the warm background color temperature) — preventing the shadow from becoming a second light source or a distraction from the product 6. A category page consistency audit — a process for reviewing all 18 generated images together in a grid layout (the format in which they will appear on the category page) before publishing, covering five visual criteria: background color temperature match across all 18, product visual weight consistency across product types, shadow treatment uniformity, colorway accuracy per product, and overall grid readability at the category page thumbnail size 7. A prompt adaptation guide for new product additions — a structured process for using the master background and lighting descriptor and the colorway consistency block to generate consistent product images for new ceramic products added to the collection after the initial 18 are complete, without needing to rebuild the background and lighting parameters from scratch **Write every prompt component as a product photography brief rather than an artistic direction — the goal is not creative expression but production consistency, and every parameter must be specified with the precision that a product photographer would use to brief a studio assistant on maintaining identical setup conditions across an 18-product catalog shoot.**