All Modules
Self-paced, free learning tracks built for AI trainers and annotators.
Everyone starts here. No prior knowledge needed.
How AI Training Jobs Work
Find out what AI annotation is, what the work looks like day-to-day, what separates people who get consistent work from people who don't, and how to figure out which income tier you can target.
Passing the Screening Process
Most candidates fail screening not because they lack the skills, but because they don't know what is being tested. This module breaks down every stage, the most common failure modes at each, and how to prepare for the real thing.
Your First Annotation Task
Walk through a complete pairwise comparison task step by step. By the end you've made a real annotation decision, written a rationale, and know what quality means in practice.
Essential skills every annotator needs regardless of specialisation.
Foundations of RLHF
Understand the full RLHF pipeline — from pretraining to PPO — and learn exactly what annotators do, why quality signals matter, and how your feedback shapes model behavior.
Ground Truth Research Method
Learn how to research claims rigorously, build verifiable rationales with primary sources, and meet the evidence standards required for high-paying expert reviewer roles.
Advanced Instruction Following
Master the skill of parsing complex, multi-part instructions, handling constraint conflicts, and evaluating whether AI responses genuinely follow instructions or only appear to.
Markdown & JSON Formatting Mastery
Learn the formatting standards that AI training platforms enforce: proper Markdown, valid JSON, structured outputs for tool use. Discover how formatting errors degrade model training.
Take this if you'll work on Python, SQL, or data evaluation tasks.
Python for AI Training
Learn what coding interview tasks and code review sessions test: correctness, complexity, edge cases, and style. Discover how to evaluate AI-generated Python as a professional annotator.
SQL & Data Handling
Build the SQL and data reasoning skills needed to evaluate AI-generated queries, spot logic errors in data analysis, and succeed in data science interview tasks.
Only take the track that matches your background — you don't need all of them.
Creative Writing & Style Guide Adherence
Learn what creative writing tasks in AI training require: style guide adherence, prompt engineering, brainstorming quality, red teaming for creative content, and what writing interviewers look for.
Medical Scribing & HIPAA for AI
Understand clinical case study format, core medical terminology, HIPAA's implications for AI training data, and how to evaluate AI medical responses against provided sources — even without a clinical background.
STEM & LaTeX (Math Solving)
Learn to write and evaluate mathematical reasoning with LaTeX, verify AI-generated STEM solutions step by step, and understand what olympiad-level and quantitative reasoning tasks require.
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