An AI tool could finish your annotations faster. What would you check before using it?

Instruction: Use the supplied permission contract and inspect all three prelabels against guideline version 3. Return tool decisions, retained originals, accept/correct/review records and a short audit note.

Context:

Audit three AI prelabels under an explicit tool-permission contract. Keep a correct suggestion, repair a qualifier error and preserve an unresolved item for review.

Fictional practice task

This is a work-sample exercise, not an employer test. Guideline version 3 permits annotation assistance under this exact tool contract:

  • Built-in Prelabel Assist 1.3 is approved inside the project workspace to suggest labels for these items. An annotator must inspect each source text and record a decision; suggestions cannot be bulk-submitted as verified labels.
  • QuickLabel AI, an external service offered through a personal account, is not approved. No project text, screenshots or records may be uploaded to it.
  • Keep the original suggestion, the chosen action, the resulting label and its reason in the approved project record. No submission has occurred yet.

Allowed labels:

  • request_refund: an explicit, unnegated request to return the payment or issue a refund.
  • other_request: an explicit request for a different action, with no unnegated refund request.
  • needs_review: the text does not establish which of the two request labels applies. This routes the item for review; it does not certify that review is complete.

Source items and built-in suggestions:

  • P1: “I want a refund for this order.” Original prelabel: request_refund.
  • P2: “I do not want a refund; I only need a copy of the receipt.” Original prelabel: request_refund.
  • P3: “Can we sort this out?” Original prelabel: other_request. No earlier conversation is supplied.

Produce: A tool-use decision for both services. For each item, retain the original prelabel and choose accept, correct or review, then give the resulting allowed label and source-based reason. accept and correct can be ready for normal submission; review remains pending review. Finish with a short audit note naming the guideline and approved tool version. Do not claim the items were submitted or that a pending item was reviewed.

Updated

Prepare a stronger answer

I’d use the approved built-in suggestions as a starting point, but I wouldn’t use QuickLabel AI or upload the project text to a personal account. For P1, I’d accept request_refund because the source explicitly asks for a refund...

This member answer includes:

  • • A complete, copyable sample answer
  • • Guidance for adapting the answer to your experience
  • • A practical walkthrough
  • • Common mistakes and how to avoid them
  • • Answered interviewer follow-ups
  • • Strong, adequate and weak assessment criteria
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Your preparation path

Choose the track that matches the role. Work through its questions in order, then explain each answer in your own words.

1. Entry level annotation

Apply guidelines, label text and spans, and explain a small practice project.

  1. How would you explain the data annotator role and the kind of work you would expect to do? Free sample
  2. How would you learn a new annotation guideline before starting your first batch? Member answer
  3. Apply a sentiment guideline to four short comments. Which labels would you choose, and why? Free sample
  4. Mark two location mentions using the exact character-offset contract. How would you check your result? Free sample
  5. Walk me through an annotation or quality-checking project you can discuss, including your own contribution and limits. Member answer

2. AI response evaluation

Compare responses using separate criteria for correctness, instruction following, and writing quality.

  1. Compare two AI responses against a supplied fact sheet. Which response is better under the rubric? Free sample
  2. One response is accurate but breaks the required format; another follows the format but contains a false claim. How would you rate them? Member answer
  3. Write a short rating rationale that identifies the decisive error without restating both responses. Member answer
  4. An AI response includes a factual claim you cannot verify from the supplied sources. What would you do? Member answer
  5. Two responses have different strengths and neither clearly wins. How would you apply the ranking rules? Member answer

3. Senior review and quality

Work through disagreement, missed critical cases, changing guidelines, review capacity, and reviewer calibration.

  1. Two experienced reviewers disagree repeatedly, and the deadline leaves little time for adjudication. What would you recommend? Free sample
  2. A batch has 98% accuracy against reviewed references but misses every critical item. Would you accept it? Member answer
  3. A labeling rule changes halfway through a delivery. Would you relabel old work, split the dataset or delay the release? Member answer
  4. The delivery requires review of every item, but the available reviewers cannot finish by the deadline. What would you change? Member answer
  5. A reference answer appears to contradict the written rule, and workers are being penalized for disagreeing with it. What would you do? Member answer

Try the 20 minute mock assessment. Use the fictional cases to practice; the self-check is not an employer's hiring benchmark.

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