Choose Where Human Review Helps

Choose the question, reviewers, and evidence that fit your AI workflow. These examples show how human feedback helps improve communication, research, job writing, and published content.

Customer replies

Illustrative example

Draft reply: “Your package is delayed. Please keep checking the tracking page.”
Human check: Would you send this response to the customer as written?
Panel result: Revise — 4 of 5 reviewersDoes not acknowledge the inconvenienceNo support option offered

The owner adds an apology and a support option before the reply is sent.

When to check: Before the draft is sent — especially after an agent change, or when low confidence or missing context calls for review.
Who assesses: Rating AI rates the reply by default, alone or with invited reviewers. Invited colleagues check policy correctness, clarity, and tone.
What you get back: AI returns an advisory rating; human review adds reasons and disagreement. The support owner sends, revises, or escalates.

Research and client work

Illustrative example

Draft conclusion: “Churn fell 18% because of the new onboarding flow (source: Q2 cohort dashboard).”
Human check: Is this conclusion supported by the supplied sources?
Panel result: Not supported — 3 of 5 reviewersPricing changed the same quarterCorrelation only

The owner weakens the claim and adds the caveat before the report goes out.

When to check: Before the deliverable reaches a client or informs an important internal decision.
Who assesses: Invite colleagues with the knowledge needed to judge correctness, clarity, or source credibility.
What you get back: Reasons, each tied to the source a reviewer checked. The owner delivers, revises, or escalates; a pilot tracks unsupported-claim flags and acceptance.

Job and HR text

Job and HR text quality only. Do not use this template to screen, score, rank, or recommend applicants; those workflows are outside the public Alpha.

Illustrative example

Draft job posting: “We seek a young native German speaker. Please include a photo and family status.”
Human check: Is this text factual, non-discriminatory, and free of impermissible requirements?
Panel result: Revise — 4 of 5 reviewersAge and native-language wording need reviewPhoto and family-status request is unsupported

The hiring team removes the flagged wording and checks the revised posting before publication.

When to check: Before publishing the job text, and after a material change to the role or draft.
Who assesses: Authorized HR or legal editors who can review the text and the approved role requirements. Do not submit applicant records.
What you get back: Reasons and a revision recommendation bound to the exact draft. The hiring team remains responsible for the final posting.

EU AI Act · applicant analysis boundary

AI used to analyse applications or evaluate candidates is listed in Annex III. For systems that qualify as high-risk under Article 6, Article 14 will require effective, proportionate human oversight and Article 26 will require deployers to assign people with the necessary competence, training, authority, and support when these rules apply from 2 December 2027 under Regulation (EU) 2026/1744. They do not require a panel to approve every recommendation: the applicable oversight measures depend on risk, context, and the provider's instructions. Employers remain responsible for classification, lawful handling of applicant data, designated oversight, hiring decisions, and compliance in those separate workflows. GDPR Article 22 separately already restricts solely automated decisions that have legal or similarly significant effects.

Annex III Article 6 Article 14 Article 26 Regulation (EU) 2026/1744 GDPR Article 22

Published editorial content

Illustrative example

Draft article: “The new treatment works for almost everyone with this condition.”
Human check: Is this claim supported by the cited study and publishable as written?
Panel result: Revise — 4 of 5 reviewersOverstates one small trialOmits the stated contraindication

The editor narrows the claim and records the review before the article is published.

When to check: Before the text is published, and after a material model, prompt, house-style, or workflow change.
Who assesses: Editorial or subject-matter owners for accuracy and house style; general readers for clarity. A named person keeps editorial responsibility for the publication.
What you get back: Reasons, disagreement, and timing, bound to the exact version reviewed. The named editor publishes, revises, or holds the text.

EU AI Act · transparency duty

Article 50(4) applies to a deployer who publishes AI-generated or AI-manipulated text for the purpose of informing the public on matters of public interest: that deployer must disclose that the text was artificially generated. The duty does not apply where the content has undergone a process of human review or editorial control and a natural or legal person holds editorial responsibility for its publication. Article 50 was not deferred by Regulation (EU) 2026/1744 and has applied since 2 August 2026. Its scope is narrow and context-dependent: labels such as internal documentation, proposal, customer-service reply, or marketing copy do not by themselves decide whether a publication is in scope. RateLoop can run and record a substantive review and export its evidence; deciding whether a publication is in scope, and who holds editorial responsibility, remains yours.

Article 50 Article 50 guidance Regulation (EU) 2026/1744

Automate what is objective

Keep unit tests, schema validation, deterministic policy checks, tracing, and automated evaluators. Route the contextual question they cannot settle—clarity, appropriateness, usefulness, or support for a conclusion—to people. The same pattern covers classification and extraction exceptions: when ambiguous source material or low confidence makes a structured result unreliable, route that case to reviewers who can read the source. Human results can also calibrate automated evaluators; they do not turn a subjective judgment into an objective fact.

Choose reviewers and material separately

Reviewer qualifications determine who can answer the criterion. Data sensitivity independently determines what each reviewer may receive. Reviews are limited to people your workspace invites. Use only public, synthetic, or safely redacted material in the Alpha. Never treat an invitation as proof of expertise, independence, residence, or suitability for every task.

When RateLoop is not the right tool

Use deterministic automation when it can settle the question reliably.
Do not use a panel as an emergency control or when the response window is too slow.
Do not share material the selected reviewers are not authorized to see.
Do not ask reviewers to decide what the supplied evidence or their expertise cannot support.
Do not treat the panel as the sole medical, legal, financial, security, or safety approval.

After a model, prompt, tool, or workflow change, review returns to the saved starting coverage for the changed scope. A saved balance of 100 requires human review for every eligible output; previously saved Stakes policies keep their historical schedule until changed explicitly. How It Works explains that calibration and the review and evidence lifecycle. For connection and policy-bound integration, continue with Agents & MCP.