Foundation · Free
AI Data Annotation Foundations
A free introduction to paid AI data work: what annotators actually do, how to read a rubric, how edge cases are judged, and why most new annotators get removed from projects in week one.
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What to expect
- Written lessons with worked examples rather than video, so you can search back to a rule while you are working
- A practice set of 20 ambiguous items where the reasoning is explained, not just the correct answer
- Short checkpoints at the end of each module that you mark yourself against a model answer
- No deadlines, no live sessions, and no software to install
What is included
- 20 practice items with explained answers
- A glossary of the terms used in project briefs
- A one page rubric reading checklist you can keep beside you on a live task
Who this is for
- Anyone who has never done paid annotation work and wants to know what it involves
- People who keep getting screened out and cannot tell why
- Anyone deciding whether this work is worth pursuing before spending money on it
Skip it if
- Experienced annotators who already work on evaluation or preference projects
- Anyone looking for a list of platforms to sign up to, which the job board already covers for free
What you will be able to do
- Explain what annotation, evaluation, and RLHF work involve day to day
- Read a labeling rubric and apply it consistently across ambiguous items
- Recognise the edge cases reviewers use to score your accuracy
- Balance throughput against quality without failing spot checks
- Use the vocabulary that appears in project briefs and rejection notices
- Judge whether a platform is worth your time before you commit to it
Syllabus
5 modules, 20 lessons, about 4 hours of work in total.
- 01
What AI data work actually is
40 minThe task types labs pay for, who buys them, and how projects are structured.
- The four families of work: labeling, evaluation, preference, and written data
- Who is buying: labs, vendors, and platforms, and where the money comes from
- How a project is structured, from pilot batch to production queue
- Reading a project brief and deciding whether to take it
- 02
Reading a rubric like a reviewer
50 minTurning vague guidelines into repeatable decisions, and what to do when the rubric is silent.
- Anatomy of a rubric: definitions, scales, and worked examples
- Turning a vague instruction into a rule you can repeat tomorrow
- What to do when the rubric does not cover your item
- Asking a question that gets answered, and when to just decide
- 03
Edge cases and breaking a tie
50 minThe disagreement patterns that drive most quality scores, with practice items.
- The five disagreement patterns behind most quality scores
- Breaking a genuine tie without inventing a new rule
- Practice set: 20 ambiguous items with explained answers
- Recording a decision so a reviewer can follow it
- 04
Speed, accuracy, and QA
55 minHow spot checks and gold sets work, and why rushing costs more than it earns.
- How gold sets, spot checks, and audits actually sample your work
- The arithmetic of accuracy: why a small error rate ends a project
- Finding your sustainable pace instead of your fastest one
- Reading a quality report and fixing the right thing
- 05
Staying on a project
45 minWhy most new annotators are removed in the first week, and what the ones who stay do differently.
- The removal reasons that appear again and again
- Handling a guideline change mid project without losing consistency
- Working with reviewer feedback rather than arguing with it
- Getting invited back: what a good contractor record looks like
Roles this prepares you for
Free reading first
These guides are free and cover some of the same ground. Read them before paying for anything.
Questions about this course
Is this course really free?
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Yes. It exists so you can judge whether this work suits you before paying for anything, and so the paid courses do not have to spend time on basics.
Do I need any experience to start?
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No. It assumes you have never done annotation work. If you already produce preference data or evaluate model output for a living, start with RLHF and Model Evaluation instead.
Will this get me hired?
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No course can promise that. It teaches the rubric reading and quality habits that platform assessments test, which is the step most applicants fail. Applications still happen on employer sites.
Before you buy
This is training you work through at your own pace, written by DataAnnotationJobs.org. It does not guarantee a job, an assessment pass, or any level of earnings, and it is not affiliated with or endorsed by any employer listed on this site. See terms and refunds.