Before an AI-generated room render reaches a user, check the request and any supplied images, generate the image, inspect the output for safety, and apply a product policy that allows, blocks, or routes it for review. A provider’s moderation is a useful signal—not a complete release decision—and it does not verify whether the room design is accurate or useful.
What a pre-display check needs to cover
A reliable release workflow checks more than the prompt. It accounts for relevant text and image inputs, the generated render, and cases where a check or generation fails. OpenAI’s documentation for ChatGPT Images 2.5 describes checking text and image inputs and analyzing the generated image before it is shown (ChatGPT Images 2.5 safety documentation). That describes an OpenAI system; it is not evidence that every room-rendering product, or every image provider, follows the same process.
For an application, confirm what the selected provider actually checks and what its responses mean. If output moderation is not already covered in a way that meets the product’s needs, consider a separate output check before display.
A practical workflow before the render is shown
- Set the product policy. Define which cases may be shown, which must be blocked, and which need review. Treat moderation categories and scores as evidence for that decision, not as universal verdicts. OpenAI’s Moderation guide explicitly recommends using results as signals for an application’s policy.
- Check the prompt and supplied images. Send text as text and images as images to checks that support those input types. Verify the selected model’s coverage: the OpenAI Moderation guide notes that some categories are text-only, so an image-only check cannot be assumed to assess risks in a prompt or accompanying text.
- Generate the render and track its status. Record whether generation succeeded or was blocked. OpenAI’s Image Generation guide documents a
moderation_blockederror and identifies the block stage asinput,output, orunknown. Use that stage as diagnostic information; it is not, by itself, a user-facing explanation. - Check the generated image before release. Apply a suitable output moderation check or use documented provider output moderation, then evaluate its result under your product policy. Do not infer that an input check makes a separate output check unnecessary unless the provider’s documented behavior and your requirements support that conclusion.
- Route errors and uncertainty deliberately. A moderation request can return an error rather than scores. Do not treat missing results as a clean pass. Define a safe block, retry, or review path for errors and ambiguous results; the right outcome depends on your policy and product context.
- Test the whole flow. Exercise ordinary room-design prompts as well as adversarial and policy-edge inputs. Check for both false blocks and missed cases, and reassess thresholds if you rely on category scores: OpenAI says the Moderation endpoint’s underlying model may be upgraded, which can require recalibration for custom policies. Its Safety Best Practices recommends moderation, human oversight, and red-teaming with representative and adversarial inputs.
- Review visual quality separately. Set criteria for whether the render follows the design brief and meets product requirements. The cited safety guidance does not establish checks for layout accuracy, furniture placement, accessibility, or fidelity to a user’s request.
How to interpret moderation results
The OpenAI Moderation guide describes a top-level flagged result, category flags, and category scores. These can help an application decide what to do, but a flag or score is not a universal legal or product decision. Define how each result maps to your own allow, block, or review outcomes, including what happens when categories disagree or a score falls into an uncertain range.
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Coverage depends on the input type supported for each category. OpenAI’s Moderations API reference documents accepted text and image inputs and the response type; consult the current documentation for the endpoint and model you use rather than assuming every category applies to every modality.
There is also an important boundary: OpenAI says its Moderation API is not designed to detect or handle known or suspected child sexual abuse material (CSAM), and is not a substitute for dedicated child-safety safeguards. Do not submit known or suspected CSAM to that API; establish appropriate safeguards for your product instead.
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Keep safety approval separate from design approval
A render can pass a safety check and still be a poor answer to the user. A moderation classifier does not establish that the furniture is where the user asked for it, that the layout is usable, or that accessibility requirements are met. Define room-specific quality criteria separately and validate them with your own review process. OpenAI’s Image Generation guide also notes limitations in consistency and composition control, which makes visual review a distinct concern.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a workflow by its coverage and failure handling
When comparing providers or designing an internal flow, ask concrete questions rather than treating moderation as a single on/off feature:
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- Are text inputs, supplied images, and generated outputs checked?
- Which harm categories and modalities are covered, and which are not?
- Does the response expose category flags, scores, and errors clearly?
- Can the application route allowed, blocked, and uncertain cases to distinct outcomes?
- What privacy and retention constraints apply to prompts, images, and moderation results?
- Can the team carry out human review and adversarial testing?
These are useful evaluation criteria, not a ranking of providers. OpenAI’s documentation supports conclusions about its own APIs and described safety systems; it does not establish how an unnamed room-rendering product implements moderation.
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