Direct answer: an AI workflow for ecommerce teams
If your team produces main images, lifestyle shots, and campaign assets for dozens of SKUs every week, outsourcing or manual retouching scales linearly with your catalog. A more efficient approach is to turn product image production into a reusable AI workflow: a large language model first expands structured product details into complete image prompts, an image model then renders the visuals, and the same template is re-run for each new SKU.
RelayMe chains these steps in a single workflow, which suits ecommerce and design teams that need consistent images in volume. For one-off, hand-polished creatives, a traditional retouching process may still serve you better.
Step 1: define the workflow logic
Start by fixing what one workflow should produce: white-background main images, lifestyle scenes, or promotional composites. Lock down the inputs — product name, selling points, target style, aspect ratio — as workflow variables. The more structured the input, the more stable batch results become.
Keep one workflow per output type instead of mixing main images and scene images in a single flow; separation lets you tune prompts and parameters per image type and makes failures easier to trace.
Step 2: chain text and image generation
In a RelayMe workflow, the output of a language model step can feed directly into the next image generation step. A typical setup: the LLM expands product details into a full image prompt covering subject, environment, lighting, and composition, and the image step renders from that prompt.
Operators only fill in product details and never need to write prompts themselves. For categories where prompt quality is critical, add a manual confirmation step before rendering.
Step 3: use reusable templates
Once a workflow runs well, save it as a template so teammates process new SKUs with identical steps and parameters, keeping batches visually consistent. The RelayMe template library also ships ready-made product image templates you can copy and adapt.
When new products arrive, replace only the product fields and leave the structure untouched. When the style needs to change, duplicate the template into a new version so in-flight batches are unaffected.
Step 4: improve with task history
Batch generation accumulates a task record. Review the history regularly to see which prompt patterns yield the highest usable rate and which parameters trigger re-runs, then fold those findings back into the template.
A practical routine: each week, pick the image type with the lowest usable rate, change a single variable such as the lighting description, and compare the next batch. As templates iterate, manual screening time drops and new teammates can start from the latest template directly.