Annotation, quality & human feedback
Evaluate and rank model responses (RLHF / preference data)
Compare responses for helpfulness, accuracy, safety; write rationales; follow rubrics.
~6 focused hours·beginner
Tools: preference-ranking rubrics, Label Studio/internal rating tools, domain guidelines
Market relevance — share of job ads asking for this
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
Practice
Pairwise LLM response rating set with rationales
Generate 40 prompt pairs (use 2 different models or 2 prompt variants) covering a mix of factual Q&A, coding, and a Hindi-language customer-service scenario. Rate each pair for helpfulness, accuracy, and safety using a rubric you write, with a 1-2 sentence rationale per rating.
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
- Public repo with the rubric, the rated dataset, and rationales
- Reported inter-rater or self-consistency agreement number on a re-rated sample
- A short write-up of 2-3 hardest cases and how the rubric resolved them
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?