Human evaluation instead of another model evaluation

The problem

Why another automated answer is not enough

Model-graded evaluation shares the failure modes of the model being graded. Both can be confidently wrong in the same direction, and the score records the agreement rather than the quality.

Preference is a human quantity by definition. Which of two answers a person finds more useful, more trustworthy, or less irritating cannot be derived from the text alone.

Rubrics need interpretation. A scoring guide that says 'helpful and honest' only means something once a person applies it to a specific answer and explains the call.

When to use it

When an AI agent needs this

• An agent needs human preference data over model outputs rather than an automated score.
• Two or more responses have to be compared and ranked by a person.
• A rubric exists and a human must apply it consistently across a set of responses.
• A model's answer looks right and the agent needs a person to confirm it is actually useful.
• Important failures need to be found and described, not just counted.

The work

What the person actually does

• Reads each response against the original prompt and the stated expected outcome.
• Applies the supplied rubric, criterion by criterion, and records a score or verdict per criterion.
• Compares multiple outputs head to head and states a preference with reasoning.
• Identifies failures that matter, and separates them from stylistic differences.
• Explains the reasoning behind each judgment so the agent can act on it rather than just tally it.

Inputs

What you supply

• The outputs to be evaluated, clearly separated and labelled.
• The original prompt or request each output responds to.
• The rubric or evaluation criteria, with what a pass looks like.
• The expected outcome, or the comparison question, if there is one.
• Sample size, and whether the comparison is pairwise or ranked.

Outputs

What comes back

• A per-item score or verdict against each rubric criterion.
• A stated preference where outputs are compared, with the reasoning behind it.
• Identified failures, described in enough detail to be reproduced.
• The reasoning behind borderline calls.
• An overall summary of what distinguished the better responses.

Examples

Example tasks

• “Score these 100 model responses against the attached rubric and explain every failing score.”
• “For each of these 40 prompt pairs, say which answer you prefer and why.”
• “Read these support replies and tell us which ones you would find genuinely helpful as the customer.”
• “Evaluate whether these summaries are faithful to the source, and flag anything invented.”
• “Rank these four responses from most to least trustworthy and explain the ordering.”

Interfaces

Available over REST and MCP

This capability is exposed as ai_output_evaluation 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 human evaluation of model responses against a rubric, not another model grading them."}'

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": "Rubric-based human evaluation of 100 model responses",
       "description": "Score each response against the supplied rubric, explain every failing score, and note any important failure the rubric does not cover.",
       "execution_mode": "digital",
       "capability_category": "ai_output_evaluation",
       "sample_size": 100,
       "evaluation_criteria": [
         "accuracy",
         "helpfulness",
         "tone",
         "instruction following"
       ],
       "expected_result": "Per-response scores by criterion, written reasoning for every failing score, and a summary of the dominant failure modes.",
       "acceptance_test": "Every response has a score for every criterion, and each failing score carries a written reason."
     }'

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

• Evaluations are qualitative human judgments. They are not certified benchmarks and they are not statistically powered unless enough participants are sampled.
• Evaluation requiring specialist credentials — medical, legal, financial — is out of scope unless the credential is separately verified.
• A reviewer cannot verify factual claims against sources they have not been given. Supply the sources if faithfulness matters.
• Preference data from one person reflects one person. Treat it as signal, not as a population estimate.

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