01
Step 1
Take the product's reference image, the intended usage setting, the audience description and the rights record covering any people or recognisable props, together with the output sizes each placement needs.
Back to topic
Spark playbook scenarios · Spark: AI Store Assistant
Place a product in a relevant lifestyle scene
The gallery shows the product only on a plain background, which gives buyers no sense of how it is used. In this case, the immediate task is to place a product in a relevant lifestyle scene. The workflow should produce a scene brief or generated asset that keeps the product identical to its reference while the setting and props show one real use.
Topic
Product Image Generation
Target keyword
product lifestyle image generator
Copyable AI prompt
A prompt you can drop directly into Spark
First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.
ai-prompt.txt
Act as a Shopify operations specialist.
Help me place a product in a relevant lifestyle scene.
My store context is [describe the business, products and target audience].
My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [the product reference image, intended usage setting, audience description, rights records for people and recognisable props, and required output sizes per placement]. Define one scene per asset covering setting, props and lighting that shows a single real use the product supports, keep the product itself matching its reference, and flag any staging that implies conditions the specification does not back.
Return the scene brief or generated asset for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.How to solve
A recommended resolution workflow
Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
02
Step 2
Define one scene per asset covering setting, props and lighting that shows a single real use the product supports, keep the product itself matching its reference, and flag any staging that implies conditions the specification does not back.
03
Step 3
Deliver the scene brief or generated asset, compare the product region against its reference for drift in shape, colour or markings, check the scene claims against the specification, and log both checks' outcomes with the asset.
FAQ
Common questions when running this scenario
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
What keeps a lifestyle scene honest?
The scene is checked against the product specification before generation, so any setting suggesting durability, scale or environment the item does not support is flagged and replaced; the scene may add context but must never add claims the record cannot back.
When may people appear in the scene?
Only when the rights record covers the person and the audience description supports their presence; otherwise the scene is reworked around the product alone, because an unusable likeness creates a licensing exposure the image does not justify.
How is the product region checked after generation?
The product area alone is compared with its reference for shape, colour and marking drift, separate from the scene around it; only drift-free products pass, since the setting is decorative while the item is what the buyer receives.
Related scenarios
Continue with adjacent or same-topic scenarios
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
Next step
Want these AI scenarios embedded directly into daily operations?
Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.