What this prompt does
This prompt turns a model into a systematic image-generation workflow designer for a [project_type] built on Gemini [model_version]. Rather than asking for one image, it walks through six stages: a prompt-template library across [image_category_count] categories ([image_categories]), a style-consistency prefix, a batch pipeline in [sdk_language], an iterative refinement loop, content-policy screening, and a review-and-export step.
The structure works because it separates the creative decisions (style, palette, composition) from the engineering ones (rate limiting, retries, storage). The style guide variables — [default_style], [primary_color], [secondary_color], [accent_color], and [aspect_ratio] — are prepended to every generation so a batch of [batch_size] images stays visually coherent instead of drifting frame to frame. The [refinement_rounds] value caps how long the loop spends polishing each prompt before picking a winner, which matters because each round is another paid generation.
The deliberate split between creative templating and pipeline engineering is what keeps this usable at scale. The refinement step closes the loop by feeding the gap between desired and actual output back into a text model to suggest prompt improvements, so the prompt library gets smarter over time rather than starting fresh each run. The policy-screening stage in front of [content_policy] means fewer wasted calls on prompts that would be blocked anyway, and logging those blocks turns failures into template fixes.
When to use it
- You need on-brand visuals at volume rather than a handful of one-off images.
- A project spans several image types and you want each one templated, not improvised.
- You are wiring Gemini or Imagen into a
[sdk_language]batch job with storage and retries. - You want a human review gate before images ship to a
[audience]. - You need prompts pre-screened against
[content_policy]to avoid blocked generations. - You want a manifest tying each image back to its prompt, parameters, and license terms.
Example output
Expect a staged build plan: a set of reusable prompt templates (one per category with placeholders for subject, style, palette, lighting, mood), a style-guide prefix block, pseudocode or real [sdk_language] for the batch pipeline with rate limiting against [rate_limit] and writes to [storage_location], a refinement loop description, a policy-screening routine, and a gallery review flow that exports approved files in [export_formats] with a metadata manifest.
Pro tips
- Fill
[image_categories]with the exact assets your project needs; vague categories produce vague templates. - Set
[batch_size]realistically against[rate_limit]so the pipeline does not stall on quota errors. - Keep the three color variables as real hex values so the style prefix enforces palette, not a description of it.
- Lower
[refinement_rounds]for cheap drafts and raise it only for hero images, since each round costs another generation. - Treat
[content_policy]screening as a pre-filter, not an afterthought; logging blocked prompts builds a better template library over time. - Verify which Gemini
[model_version]actually supports image output in your account before building around it.
