Check the request and any supplied images, generate the room render, then review the generated image before displaying it. A provider’s moderation is one useful safety signal—not a complete release decision. Your application still needs clear allow, block, and review rules, a safe path for errors, and separate checks for whether the design actually meets the brief.
What to check before a room render reaches a user
Use a release-control flow with checks at more than one point: assess the user’s text and supplied images, generate the render, and inspect the output before showing it. OpenAI’s documentation describes input checks and a final analysis of generated images in the ChatGPT Images 2.5 safety system, but that is evidence about OpenAI’s system—not proof that every image provider or room-design app follows the same process. Confirm what your chosen provider actually checks.
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OpenAI’s Moderation guide describes results such as an overall flagged value, category flags, and category scores. Treat those results as inputs to your application’s policy, not as a universal verdict that content must be shown or blocked. The guide also notes that some categories are text-only, so checking an image alone cannot be assumed to assess risks in the accompanying prompt.
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The steps below are an implementation pattern based on the cited platform guidance. They are not a claim about the workflow of any particular room-rendering product.
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- Set policy and outcomes. Define which cases are allowed, blocked, or sent for review. Decide how moderation categories and scores inform each outcome, and document the rules for your team.
- Check the request and supplied inputs. Assess relevant user text as text and supplied images as images, using a provider and model that support the checks you need. Verify the supported input types: an image-only check may not cover risks expressed in text.
- Generate and record the result. Track whether generation succeeded or was blocked. OpenAI’s Image Generation guide documents a
moderation_blockederror and identifies its stage asinput,output, orunknown. Keep this information for diagnosis; it is not, by itself, a user-facing explanation. - Review the output before display. Use the provider’s documented output moderation where available, or add an appropriate output check, then apply your own policy. OpenAI’s Moderation guide says results should be reviewed before generated content is shown; its Image Generation guide also describes provider filtering.
- Handle uncertainty deliberately. If moderation returns an error or its result does not fit a defined policy outcome, route the render to review or block it according to your policy. Do not treat missing scores as approval.
- Test the whole flow. Try ordinary room-design requests as well as adversarial and policy-edge inputs. Check for both false blocks and missed cases, and revisit thresholds when the moderation model changes. OpenAI notes that the endpoint’s underlying model may be upgraded, which can mean score-based custom policies need recalibration.
- Assess design quality separately. Define checks for whether the render follows the brief and meets your product’s requirements. A safety classifier does not establish that a layout is accurate or usable.
Plan for errors and cases that need escalation
The Moderation guide documents that a call can return an error instead of scores. Build a defined fallback into the release flow: depending on your policy, that may mean a safe retry, a block, or human review. The right choice depends on the product, but silently treating an unavailable result as a clean pass is not a reliable control.
For image generation, preserve the documented block-stage information—input, output, or unknown—as diagnostic context. Use it to understand where a request stopped, while keeping any explanation shown to the user clear and appropriate for your product.
There is also an important child-safety 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.
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Passing a moderation check does not show that a room render is correct. The reviewed vendor guidance does not set criteria for accurate room layouts, furniture placement, accessibility, or fidelity to a design brief. Define those acceptance criteria separately and decide whether they need automated checks, human review, or both.
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OpenAI’s Image Generation guide also notes limitations in consistency and composition control. A safe image can still have a misplaced sofa, an unusable circulation path, or details that do not match the request; those are quality issues for your product to evaluate, not conclusions a safety score can establish.
Test, monitor, and maintain the policy
OpenAI’s Safety Best Practices recommends moderation, human oversight, and red-teaming with representative and adversarial inputs. Apply those practices to the full room-render journey: user text, supplied images, generation, output review, and the final display decision.
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There is no universal moderation threshold established for room renders in these materials. Choose thresholds and escalation rules for your own policy, evaluate their behavior against realistic cases, and reassess them when the model or product changes. OpenAI’s guidance supports moderation and oversight but does not prescribe room-specific acceptance criteria or a single correct policy.
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OpenAI’s ChatGPT Images 2.5 safety documentation describes checks on text and image inputs and a final analysis of generated images before display in that safety system. It does not establish that an unnamed room-rendering product—or every provider—uses those checks. For another system, verify its input and output coverage, supported modalities, result fields, error behavior, and options for review rather than assuming parity.
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The Moderations API reference documents accepted text and image inputs and the response type. When evaluating a provider or workflow, also ask what data is retained and under what privacy constraints, and whether the system can support your allow, block, and review paths. The cited materials do not rank providers or provide a room-render-specific performance rate.
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