Label Studio
Data · Test
Label examples, compare annotations and export a dataset for review or evaluation.
All capabilities · Annotation, quality & human feedback
Annotate and evaluate in an Indian language
Be the native-language expert on a dataset: label, transcribe, rate and culturally adapt content in Hindi, Tamil, Bengali, Malayalam or another Indian language, and judge whether a model's output is fluent, accurate and culturally appropriate for that audience.
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…
- Label, transcribe or translate text and audio in your language against a written spec
- Judge whether a model's answer is fluent and natural to a native speaker, not just grammatically valid
- Catch cultural mistakes: wrong register, wrong honorific, an example that makes no sense in India
- Handle code-mixed text (Hinglish, Tanglish) consistently instead of guessing case by case
- Write the rationale for a rejection so a reviewer who doesn't speak the language can follow it
- Flag ambiguity in the guidelines rather than silently inventing your own rule
- Keep annotation speed and accuracy inside the quality bar the vendor sets
Needs first: Label data accurately against guidelines
Learn — free, link-checked
The few resources that matter
Read · beginner · 30 min · labelstud.io
Get started with Label Studio
Official quickstart for installing Label Studio and configuring your first labeling project with hotkeys. — HumanSignal / Label Studio
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 · 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
- Native-language judgement
- Language-specific guidelines
Practice
Rate 50 model answers in your language and write the rubric
Pick a language you are native in. Write 50 everyday Indian questions — a PF withdrawal, a train booking, a school admission, a recipe — and put them to a free chat model. Rate each answer for accuracy, fluency and cultural fit on a 1-5 scale, writing down the rule you used every time you deducted a point. Turn those rules into a one-page rubric a second rater could apply and land on your scores.
Start from
50 everyday questions you write yourself in your own language, answered by a free chat model
Milestones
- Write the 50 questions and collect the model's answers · ~1h
- Score all 50 on accuracy, fluency and cultural fit, noting the rule behind every deduction · ~2h
- Turn those rules into a one-page rubric with a worked 5, 3 and 1 · ~1h
- Write up the failure patterns specific to your language · ~0.5h
Done when
- 50 prompts and answers saved in a sheet with three scores each
- A one-page rubric with a worked example of a 5, a 3 and a 1
- At least five cases where the answer was fluent but culturally wrong, with your reasoning
- A short note on where the model was weakest in your language
Prove it
Evidence a recruiter can check
- The 50 rated samples with three scores each, published so a second rater could score them and compare
- A one-page rubric with a worked example of a 5, a 3 and a 1, specific enough that someone else applies it the same way you did
- Five cases where the answer was fluent but culturally wrong, each with reasoning a non-speaker of the language can follow
- A short write-up of where the model is weakest in your language and what that costs a user
Signal it
Rated 50 model answers in my native language against a three-axis rubric I wrote and published, surfacing the cases where fluent output is still culturally wrong — the work I show alongside my annotation-vendor qualifications.
Interview
Questions you'll get asked
- How would you label a sentence that mixes Hindi and English in the same clause?
- A model's Tamil answer is grammatically correct but reads like a translation. Do you pass it? Why?
- The guideline doesn't cover a case you keep seeing. What do you do?
- How would you explain a rejection to a reviewer who doesn't speak the language?
- What makes an answer culturally wrong even when it is factually right?