An LLM can help moderate negative reviews without treating criticism as a violation—but only if its authority is limited. In Corneliu Croitoru’s account of the travel site Back From My Trip, negative sentiment alone is never grounds for rejection: the model may approve a text submission or send it to a human for review, while an administrator confirms or reverses an AI rejection. That is a description of one implementation, not independent evidence of how accurately it works.
Why “bad reviews” are not automatically bad content
Croitoru’s September 13, 2026 DEV Community post uses “bad reviews” to mean negative travel reports and criticism, not fabricated or abusive reviews. Its moderation prompt says: “Negative reviews are ALWAYS allowed. A harsh critique of a hotel/destination is legitimate content.” The distinction is important: sentiment is not the same as whether a submission violates the site’s rules.
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The post describes moderation for a travel site, not a general-purpose guarantee that an LLM can reliably distinguish every legitimate complaint from harmful content. Croitoru’s summary of the text-review design is: “The model can flag. It cannot silence.”
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How the text-review workflow works
The implementation account describes records moving through four states: pending, approved, needs_review, and rejected. Public readers can see approved records; authors can see their own submissions and the status or reason. The post says database row-level security controls public access.
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The model can approve or escalate
For text, the model can approve ordinary submissions or route questionable ones to needs_review. The post says an AI rejection is also shown to an administrator, who can confirm or reverse it. The model therefore does not make the final rejection decision described for text reviews.
Uncertainty and failure lead to review
Croitoru says transient errors are retried three times through a job queue. If the call still fails, or the moderation budget is exhausted, the submission goes to needs_review rather than being approved. This is a fail-to-review policy: a technical failure delays publication and requires attention instead of silently passing the text through.
Prompt manipulation is escalated, not declared impossible
The author’s policy sends text for human review when it addresses the moderator, claims to be a system or administrator instruction, requests a particular verdict, or otherwise resembles a prompt. That is a guardrail in the described workflow, not proof that prompt manipulation cannot succeed.
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The post describes a database trigger that queues a row identifier and a worker that processes up to 10 jobs a minute. Croitoru also says the moderation function reads the stored text and rejects calls that lack the service role. These are implementation details he reports, not measured performance results or general recommendations.
- Moderate the stored submission: Reading the database text is intended to keep the content reviewed by the worker tied to the record rather than relying on text supplied by a caller.
- Reset after edits: Editing text returns its moderation status to
pending, so an earlier decision does not automatically carry over to changed content. - Prevent self-approval: A separate trigger guards status fields against users setting their own approval state.
Why image moderation has a different, riskier outcome
The author describes selected image rejections—such as nudity, visible personal documents, or identifiable children—as triggers for immediate file deletion, without a review queue or appeal. He says the storage bucket is public, so hiding a database row would not prevent someone from accessing the file directly.
This choice is materially different from text moderation: an image rejected by the model is not retained for a person to reconsider. Croitoru acknowledges the false-positive risk and explains that he considers the possible harm of keeping a sensitive image greater than the loss of a mistakenly rejected one. That is his site’s design tradeoff, not a universal rule for moderation systems.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this account does—and does not—establish
Croitoru’s post is an implementation account, not an independent audit or controlled evaluation. It does not name the LLM or vision-model provider, or report precision, recall, false-positive or false-negative rates, costs, test-set results, or comparative outcomes. The described safeguards explain how the workflow is intended to behave; they do not establish how often it makes mistakes or whether it performs as intended.
The author’s stated lesson is: “When an LLM mistake cannot be undone, like silencing someone, give the model the power to escalate, never the power to decide.” In the text workflow he describes, that principle means preserving negative opinions while reserving rejection for a human decision. His image policy is a consequential exception: deletion is immediate and cannot be appealed.
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