Synthetic sample deliverables

What a useful human AI QA report looks like.

Buyers convert faster when they can picture the output. These SKU-specific examples show the kind of evidence, issue labels, corrections, and uncertainty notes a human reviewer can return without promising perfect accuracy.

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Important: these are synthetic examples for scoping and quality standards. GoHireHumans human QA can reduce mistakes and create review records, but it does not replace professional legal, medical, financial, or compliance advice.
Content

AI blog post fact-check sample

Deliverable format: claim list, label, source note, suggested correction, and risk score.

Recommendation: Revise before publishing Risk: Medium Claims checked: 12 Verified: 7 Questionable: 3 Incorrect: 2 Evidence note: Statistic in paragraph 4 is outdated; current source says 41%, not 57%. Suggested correction: Replace the unsupported market-size sentence with a sourced, dated claim. Reviewer confidence: Medium — source landscape is changing quickly.
Sources

AI citation and source verification sample

Deliverable format: source existence, support level, broken links, and replacement source ideas.

Recommendation: Fix citations before using Links checked: 9 Broken links: 1 Source supports claim: 5/9 Weak or mismatched support: 3/9 Issue example: Link 6 discusses onboarding cost, not retention improvement. Suggested action: Replace Link 6 or soften the retention claim. Reviewer confidence: High for link checks; Medium for domain interpretation.
Customer support

AI support reply QA sample

Deliverable format: accuracy, policy fit, tone, escalation flag, and corrected response.

Recommendation: Revise before sending Risk: Medium Issue 1: Refund promise exceeds policy. Evidence: Policy says refunds require supervisor approval after 30 days. Suggested wording: “I can escalate this for review and follow up with available options.” Tone: Empathetic, but needs a clearer next step. Escalate: Yes — billing/refund decision.
RAG

RAG answer groundedness sample

Deliverable format: answer/source comparison, groundedness label, missing context, and refusal flag.

Recommendation: High risk Grounded claims: 4/7 Unsupported answer: cites Source B for a claim not present in retrieved docs. Broken or weak citations: 2 Missing context: Source A includes an exception the answer omits. Should-have-refused: No, but answer should include caveat. Correction: Cite Source A paragraph 3 only for the supported portion. Reviewer confidence: Medium — retrieved source set may be incomplete.
Website QA

AI-built website QA sample

Deliverable format: device notes, broken links, confusing copy, trust gaps, and conversion fixes.

Recommendation: Fix before launch campaign Devices checked: iPhone-width mobile and desktop Chrome Critical issue: Mobile hero CTA wraps below first screen. Broken links: Pricing footer link returns 404. Trust gap: No clear statement of what happens after submitting interest. Suggested fix: Move the primary CTA above fold and add “no payment collected” note. Reviewer confidence: High for observed UI issues.
AI agents

AI-agent work audit sample

Deliverable format: output review, provided context check, action approval notes, and handoff risks.

Recommendation: Human approval needed Final output quality: Good summary, weak source traceability Tool/context notes: Agent used the right CRM record but did not confirm stale contact status. Action risk: Do not send automatically; relationship-sensitive account. Approval checklist: Confirm recipient, update source date, add softer intro. Reviewer confidence: Medium — tool logs were partial.
Product content

AI product content QA sample

Deliverable format: claim review, missing product facts, grammar/tone notes, and improved copy.

Recommendation: Revise before publishing Risk: Medium Issue 1: “Best in class” claim is unsupported. Issue 2: Battery-life number needs a source or removal. Missing info: Warranty length, compatibility, return window. Suggested correction: Replace absolute claim with sourced spec or softer language. Publish status: OK after claims are sourced or softened.

Minimum quality bar

  • Pass / revise / high-risk recommendation
  • Issue severity and location
  • Evidence links, screenshots, policy references, or source notes
  • Suggested correction where useful
  • Confidence and uncertainty notes

How to use these examples

Pick the closest SKU, paste the AI output and source material into a buyer brief, then use the managed pilot page for manual scoping. No checkout or job is created automatically from this page.

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