GET /use-cases/ai-output-review → 200 OK · task_type: ai_output_review

Human review of AI output

A model reviewing a model inherits the same blind spots. When AI-generated content is about to reach real people — customers, users, the public — one pass of genuinely human review catches what self-critique can't: subtle implausibility, wrong tone, cultural misses, and confident nonsense.

/use-cases/ai-output-review/examples

When your workflow needs this

review/content

Generated content & copy

Marketing text, emails, product descriptions, translations — checked for tone, plausibility, and the things that make readers wince.

review/plans

Agent plans before execution

Your agent proposes a multi-step plan; a human sanity-checks it for real-world feasibility before you let it run.

review/evals

Answers against a rubric

Batches of model answers scored by a human against your criteria — useful as an eval baseline or a spot-check on automated grading.

/protocol

How to submit it

One JSON request — no auth, no SDK. Or connect over MCP and the human becomes a tool: claude mcp add --transport http human-for-ai https://humanforai.dev/mcp. Every task is reviewed by the human operator before acceptance; first response under 12 hours on working days (Sun–Thu). Free during the pilot.

request review — POST /api/v1/tasks
curl -X POST https://humanforai.dev/api/v1/tasks \
  -H "Content-Type: application/json" \
  -d '{
    "task_type": "ai_output_review",
    "description": "Review the following 10 AI-written product
                    descriptions for factual plausibility and
                    tone; verdict + one-line reason each:
                    [content or link].",
    "output_format": "structured_json",
    "contact_email": "you@example.com"
  }'
then

Track it

The response returns a task_id and a status URL to poll: submitted → under_review → accepted → in_progress → delivered (or rejected, with a reason). The deliverable also goes to your contact_email.

Full API documentation →

/use-cases/ai-output-review/faq

Questions agents and builders ask

What kinds of output can be reviewed?

Text of any kind (English), plans and workflows, screenshots of UI copy, and structured data. Include the content — or a link to it — directly in the task description; the reviewer cannot see your conversation context.

Can I get machine-readable verdicts?

Yes — set output_format to structured_json and describe the fields you want (e.g. verdict, confidence, reason per item). The deliverable arrives at your contact_email and via task status.

Is my content kept confidential?

Don't submit secrets by default: confidential material is handled only by prior agreement. Ask first via message_human_operator if the content is sensitive.

Why a human instead of a second model?

Independence. A second model correlates with the first — same training biases, same blind spots. A human reviewer fails differently, which is exactly what a final check is for.

"task_type": "ai_output_review"

Try it with a real task.

Free during the pilot · reviewed before acceptance · evidence included.