Beginner-friendly — Start with the free foundation

Go From AI Tool User To AI Trainer

Learn how AI data work happens: evaluate responses, create useful training examples, test model behavior, and explore real project-based opportunities.

Self-paced Journey
Learn and Practice Anytime
Free Foundation
Start Before You Enrol
66 Course Modules
Full Annotation Syllabus
Freelance Guide
Understand AI data work

Everything You Need to Turn AI Training Into a Career

The course blends clear foundations with hands-on practice, preparing you to demonstrate job-relevant AI data skills.

The BRIEF Prompting Method

Turn a vague request into a useful instruction with Background, Result, Inputs, Expectations, and Format.

66 Comprehensive Modules

Get deep coverage of RLHF, Preference Learning, Supervised Fine-Tuning (SFT), and multi-modal image, video, and 3D annotation.

Verification Before Trust

Build habits for checking sources, dates, logic, uncertainty, and the live result before relying on an AI answer.

Professional AI Data Workflows

Understand common qualification-test structures, contributor quality signals, and the habits expected in professional data work.

A Practical Build Loop

Brief the work, build one focused change, observe what happened, verify it, and record what you learned.

Continuous Updates

Our handbook and guidelines are constantly refreshed as frontier models evolve. Always stay ahead of formatting conventions.

Curriculum Blueprint

From complete beginner to expert data annotator and prompt engineer in 5 progressive phases.

Phase 1

AI Tools, Prompting & Your Workspace

Start from zero: choose the right AI tool, write prompts that produce usable work, organize projects, protect sensitive information, and run your first working build.

Highlighted Lessons:
The essential AI tool stack
Prompting with the BRIEF method
Local workspace setup
Phase 2

Ontologies, Schemas & Guidelines

Master ontology creation and schema design. Write annotation guidelines that resolve ambiguity and reduce disagreement between labelers.

Highlighted Lessons:
Defining classes operationally
Workforce models
Calibration guidelines
Phase 3

Quality Engineering & Metrics

Explore advanced QA methodologies. Understand Task-level, Dataset-level, and Contributor-level accuracy, plus metrics like Confusion Matrices, IoU, and Cohen's Kappa.

Highlighted Lessons:
Agreement calculations
Routing workflows
Three-level QA design
Phase 4

Generative AI: RLHF, RLAIF & Red Teaming

Perform supervised fine-tuning data writing, human preference pair selection (RLHF), safety alignment scoring, and adversarial red-teaming tasks.

Highlighted Lessons:
Preference model metrics
Red teaming criteria
Agent trajectory logging
Phase 5

Freelancing Blueprint & Platform Playbooks

A hands-on roadmap to passing vendor qualification exams, maximizing your quality tier, and landing remote contracts on premier platforms.

Highlighted Lessons:
Qualification preparation
Quality auditing
Sustainable work habits

Start small. Unlock everything when ready.

Begin with the practical foundations or get the complete 66-module professional journey.

Start here

Free Foundation Module

Learn the core workflow before paid enrollment opens.

Free
  • AI tools and when to use each one
  • Prompting with the BRIEF method
  • Professional workspace setup
  • Privacy and verification habits
  • Your first working project workflow
Start Free Foundation
Complete journey

Best value

AI Trainer Complete Access

The complete paid journey opens after checkout and student access are verified.

Coming soon
  • Everything in Course 1
  • All 66 professional modules
  • Data annotation, evaluation and RLHF
  • Quality, safety and red-teaming playbooks
  • Self-paced projects and future updates
Join the Interest List
Paid enrollment stays closed until checkout and student access are fully tested

Built for real learning, not AI hype

Every phase moves from explanation to practice to evidence of skill.

Begin with confidence

Start with tools, prompting, workspace setup, privacy, and a local build loop. Technical terms are introduced only when they become useful.

Learn through real workflows

Brief the work, build one focused change, inspect it in a live environment, verify the result, and record what you learned.

Frequently Asked Questions

Do I need coding experience?

No. Many AI data tasks depend on careful reading, clear reasoning, subject knowledge, and following guidelines. Some specialist roles do require coding or other technical skills.

Will the course get me an AI training job?

The course helps you understand the work, practise core skills, and navigate official platform applications. Acceptance and project availability depend on each company, your location, and your skills; we do not guarantee placement or income.

Is the course live or self-paced?

The course is currently fully self-paced, with no scheduled live sessions. You can learn, practise, and revisit modules on your own schedule. If optional live experiences are added later, they will be announced separately.