Label Studio
Data · Test
Label examples, compare annotations and export a dataset for review or evaluation.
All capabilities · Annotation, quality & human feedback
Evaluate and rank model responses (RLHF / preference data)
Compare responses for helpfulness, accuracy, safety; write rationales; follow rubrics.
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…
- Compare two or more model responses and rank them for helpfulness, accuracy, and safety
- Write a concise rationale that references the rubric, not just a gut feeling
- Apply consistent standards across hundreds of comparisons in a shift
- Catch subtle issues: confident-sounding but factually wrong, unsafe, or off-policy responses
- Rate responses in a specific language/locale (e.g. Hindi) for fluency and cultural appropriateness
- Distinguish 'slightly better' from 'clearly better' using the rubric's tie-breaking rules
- Escalate systemic model failure patterns you notice across many ratings, not just one-off cases
Needs first: Explain how LLMs work and where they fail
Learn — free, link-checked
The few resources that matter
Read · beginner · 30 min · huggingface.co
Evaluate
Official docs for standard NLP quality metrics — vocabulary you need to write rationales that cite concrete numbers, not vibes. — Hugging Face
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
Read · intermediate · 30 min · docs.claude.com
Develop test cases
Anthropic's own guide to writing test cases and grading criteria for evaluating model outputs at scale. — Anthropic
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
Course · intermediate · 90 min · deeplearning.ai
Automated Testing for LLMOps
Free short course on building automated eval pipelines and rubrics for LLM applications, from prompt to production. — DeepLearning.AI
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 Sheets
Data · Plan & explain
Build a scoring sheet, clean a small dataset or make assumptions visible in a simple model.
Google Docs
Plan & explain
Write a rubric, project story or decision brief that others can review and comment on.
Practices & references
- Preference-ranking rubrics
- Domain guidelines
Practice
Pairwise LLM response ratings with a written rubric and rationales
Sample 40 prompts from the Anthropic hh-rlhf dataset covering factual Q&A, coding and open-ended help, and add a few Hindi-language customer-service prompts of your own. Generate two responses for each — from two different models, or two prompt variants of the same model — then rate every pair for helpfulness, accuracy and safety against a rubric you write first, with a rationale per rating that cites the rubric. Rate blind where you can, and re-rate a sample later to see how stable you actually are.
Start from
Milestones
- Write the rubric — criteria, scale, tie-break rule — before seeing any responses · ~1h
- Sample the prompts, add the Hindi ones, generate both responses per prompt · ~1h
- Rate all 40 pairs with a rubric-citing rationale each · ~1.5h
- Blind re-rate 10 pairs, compute agreement, write up the hardest cases · ~1h
Done when
- A written rubric (criteria + scale + tie-break rule) exists before rating begins
- All 40 pairs are rated with a clear winner/tie and a rationale that cites the rubric
- At least 5 of the 40 involve the Hindi-language scenario and are rated for fluency/cultural fit specifically
- A second rater (or you, a week later) re-rates 10 pairs blind and agreement is reported
Prove it
Evidence a recruiter can check
- The 40 rated pairs with a rationale per rating that names the rubric criterion it turned on — not 'response A is better'
- The rubric written before rating, with its tie-break rule, committed ahead of the ratings in the history
- Your blind re-rate agreement number on the 10-pair sample, with the pairs you flipped and why
- A write-up of the two or three hardest calls, including the Hindi ones, and how the rubric resolved them
Signal it
Produced a 40-pair LLM preference dataset against a rubric I wrote — helpfulness, accuracy and safety ratings with cited rationales, plus a blind re-rate showing my own consistency.
Interview
Questions you'll get asked
- Given two AI responses to the same prompt, walk me through how you'd decide which is better.
- How do you write a rationale that would be useful to the model-training team, not just 'response A is better'?
- What do you do when both responses are wrong but in different ways?
- How would you rate a response that's fluent and confident but factually incorrect?
- How do you keep rating consistency across a full day of comparisons?
- Describe how you'd evaluate a Hindi-language AI response for quality.
- What's the difference between rating helpfulness and rating safety, and can a response score high on one and low on the other?