Label Studio
Data · Test
Label examples, compare annotations and export a dataset for review or evaluation.
All capabilities · 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.
Explore 3 tools for this projectMarket relevance
Which roles ask for this — and how often
Share of job postings in India, per role, that name this capability.
What employers mean
You should be able to…
- Use your professional training to decide whether a model's answer is actually correct in your field
- Separate 'sounds authoritative' from 'is right' — the failure mode that generalist raters miss
- Cite the standard, guideline or source that makes the answer right or wrong
- Write a rationale a non-expert reviewer can audit without your degree
- Rank two plausible expert answers and say precisely what separates them
- 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
Read · beginner · 60 min · guidelines.raterhub.com
Search Quality Rater Guidelines
The actual rubric Google trains its own quality raters on — the closest thing to a real take-home for the 'AI Quality Evaluator' roles flooding Indian job boards. — Google
Build from · intermediate · 90 min · github.com
OpenAI Evals
Open-source framework with real eval examples showing how pairwise and rubric-based model grading is actually implemented in code. — OpenAI
Build from · intermediate · 120 min · github.com
Anthropic courses
Anthropic's own hands-on notebooks for tool use, structured extraction, and grading model output quality. — Anthropic
Tools for practice
Choose a tool for the job
Start with one tool for each part of your project. You don’t need to learn them all.
3 tools to explore
Google Docs
Plan & explain
Write a rubric, project story or decision brief that others can review and comment on.
Google Sheets
Data · Plan & explain
Build a scoring sheet, clean a small dataset or make assumptions visible in a simple model.
Practices & references
- Authoritative domain sources
- A documented evaluation rubric
Practice
Build an expert eval set in the field you already know
Take the domain you have professional experience in — nursing, accounting, law, civil engineering, software. Write 30 questions a working professional is actually asked, each with the correct answer and the standard, guideline or textbook that proves it. Run them through a free model, score every response against your reference, and write the rationale for each deduction so a reviewer without your training can audit it.
Start from
30 questions from your own professional field, written by you, each with a reference answer and a citable standard, guideline or textbook
Milestones
- Write the 30 questions with reference answers and pin a citable source to each · ~2.5h
- Run them through a free model and capture the raw responses · ~0.5h
- Score every response against your reference and write the rationale per deduction · ~1.5h
- Write up the three most dangerous failure modes and publish the set · ~1h
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
- Thirty domain questions with reference answers, each pinned to a citable standard or guideline — the citations are what separate this from generalist rating
- Scored model responses with a written rationale per deduction that a reviewer outside your field can audit
- The three most dangerous failure modes you found, each with the specific answer that would have caused harm if acted on
- The whole set published in a form someone else can re-run against a newer model
Signal it
Built and published a 30-question expert eval set in my own professional field with citable reference answers, documenting the three failure modes where the model sounded authoritative and was dangerously wrong.
Interview
Questions you'll get asked
- Walk me through how you would check a model's answer in your field for correctness.
- The answer is right but would still be unsafe to act on. How do you score it?
- How do you write a rationale that a reviewer outside your domain can verify?
- Two answers are both defensible. How do you rank them?
- What does a model typically get wrong in your domain that a layperson wouldn't notice?