RoleGPSPath to your AI role
Annotation, quality & human feedback

Evaluate AI outputs as a domain expert

Use professional expertise (healthcare, BFSI, law, software, geospatial) to judge whether a model's answer is correct in that field, and write a rationale a non-expert reviewer can follow. This is what separates low-paid labelling from expert evaluation work.

~10 focused hours·intermediate

Tools: Domain reference sources, Label Studio, Mercor, Handshake AI, Surge AI

Market relevance — share of job ads asking for this
AI Data Annotator / Labeling QA 32%
What employers mean

You should be able to…

  1. Use your professional training to decide whether a model's answer is actually correct in your field
  2. Separate 'sounds authoritative' from 'is right' — the failure mode that generalist raters miss
  3. Cite the standard, guideline or source that makes the answer right or wrong
  4. Write a rationale a non-expert reviewer can audit without your degree
  5. Rank two plausible expert answers and say precisely what separates them
  6. Spot the unsafe answer in your domain — a wrong dosage, a wrong compliance step, an insecure code snippet

Needs first: Evaluate and rank model responses (RLHF / preference data)

Learn — free, link-checked

The few resources that matter

Practice

Build an expert eval set in the field you already know

Take the domain you already have professional experience in — nursing, accounting, law, civil engineering, software. Write 30 questions a working professional would actually be asked, with the correct answer and the source that proves it. Run them through a free model, score each response, and write the rationale for every deduction.

Done when
  • 30 questions with reference answers and a citable source for each
  • Model responses scored against your reference with written rationales
  • A summary of the three most dangerous failure modes you found
  • The whole set published so someone else could re-run it
Prove it

Evidence a recruiter can check

  • A public repo or sheet holding your domain eval set and results
  • Rationales that cite standards, guidelines or authoritative sources
  • Your professional credential stated alongside the eval work
  • A short post explaining where models fail in your domain
Interview

Questions you'll get asked

  1. Walk me through how you would check a model's answer in your field for correctness.
  2. The answer is right but would still be unsafe to act on. How do you score it?
  3. How do you write a rationale that a reviewer outside your domain can verify?
  4. Two answers are both defensible. How do you rank them?
  5. What does a model typically get wrong in your domain that a layperson wouldn't notice?
See where you stand for AI Data Annotator / Labeling QA