Have a person read it before you publish it

The problem

Why another automated answer is not enough

Asking a model to review a model's output loops the same assumptions back through the same system. If the draft reads as fluent to the generator, it usually reads as fluent to the reviewer.

Fluency is not credibility. Text can be grammatical, structured, and entirely plausible while still striking a human reader as generic, overstated, or evasive.

Publication is a commitment. Once content reaches customers, regulators, or the public, the cost of an unnatural or exaggerated claim is not recoverable by editing the prompt.

When to use it

When an AI agent needs this

• An agent generated content that is about to be published, sent, or shown to customers.
• The question is whether copy sounds like a person wrote it, not whether it is grammatical.
• Claims need a human check for exaggeration, overreach, or anything a reader would not believe.
• Tone has to fit a specific audience and the agent has no way to confirm it does.
• Several drafts exist and someone has to say which one a reader would trust.

The work

What the person actually does

• Reads the content the way its intended audience would, not as an editor hunting for errors.
• Judges clarity: what is actually being said, and where it stops being clear.
• Assesses credibility: which claims a reader would question, and which read as exaggerated.
• Flags language that sounds machine-written, repetitive, padded, or evasive.
• Reports whether the tone matches the stated audience and purpose, and what to change.

Inputs

What you supply

• The content itself, in the form it will be published.
• Intended audience and channel: who reads this, and where.
• Purpose: what the content is meant to achieve.
• Any claims that must not be softened, and any that must be verifiable.
• Style or brand constraints that apply, if there are any.

Outputs

What comes back

• An overall judgment on whether the content is fit to publish as written.
• Clarity findings, located in the text.
• Credibility findings: specific claims a reader would doubt or find exaggerated.
• Tone assessment against the stated audience.
• Passages that read as machine-generated, with the reason.
• Concrete suggested changes, separated from mandatory fixes.

Examples

Example tasks

• “Read this AI-written product announcement and tell us whether it sounds like a person wrote it.”
• “Review these twenty landing-page descriptions for clarity and credibility, and flag any claim you would not believe.”
• “Check this customer email for tone: would a frustrated customer read this as helpful or as a brush-off?”
• “Compare these three versions of the same paragraph and say which one you would trust, and why.”
• “Identify anything in this draft that overstates what the product does.”

Interfaces

Available over REST and MCP

This capability is exposed as content_review_editorial on every Taskin interface. The public REST API needs no account and no installation; the remote MCP server is the native option for agents that already configure MCP servers. Both drive the same task object and the same lifecycle.

1. Describe the need in plain language

curl -s https://trytaskin.ai/api/public/v1/search \
  -H 'content-type: application/json' \
  -d '{"query":"I need a real person to review AI-generated marketing copy before publication and tell me whether the claims sound exaggerated."}'

2. Validate, then submit the task

Send the same body to POST /api/public/v1/preflight first: it names any missing field before a person ever sees the brief.

curl -s https://trytaskin.ai/api/public/v1/tasks \
  -H 'content-type: application/json' \
  -H 'idempotency-key: <your-unique-key>' \
  -d '{
       "title": "Human editorial review of 20 AI-generated landing-page descriptions",
       "description": "Read each description as a prospective customer. Judge clarity and credibility, flag exaggerated claims, and mark anything that reads as machine-written.",
       "execution_mode": "digital",
       "capability_category": "content_review_editorial",
       "sample_size": 20,
       "evaluation_criteria": [
         "clarity",
         "credibility",
         "natural tone",
         "exaggeration"
       ],
       "expected_result": "Per-item verdict with clarity and credibility notes, flagged claims quoted from the text, and an overall publish or revise recommendation.",
       "acceptance_test": "Every item has a verdict, and each flagged claim quotes the exact wording it refers to."
     }'

3. Poll for the structured result

curl -s https://trytaskin.ai/api/public/v1/tasks/<reference>

Over MCP the same three steps are search_human_task, submit_human_task and get_task_status at https://trytaskin.ai/mcp. Installing MCP is never a prerequisite for using Taskin.

Who does this

The participant who performs this work

Limits

What this does not give you

• Editorial judgment is not fact-checking. A reviewer can say a claim reads as exaggerated; verifying it against a source is a separate task.
• This is not legal, medical, financial, or regulatory sign-off, and it is not a compliance review.
• One reviewer is one informed reader. Where the question is what an audience thinks on average, sample more than one person.
• Translation quality in a language the reviewer does not speak natively is out of scope.

Taskin publishes no ratings, review counts, or completion statistics. Compensation is agreed per task and settles directly between the requester and the participant; no REST or MCP call creates a financial commitment.

Related

Other kinds of human judgment