RLHF Foundation
Essential training for all RLHF work. Covers AI fundamentals, task interfaces, quality standards, and justification writing.
At a glance
- Duration
- 180 min
- Modules
- 7 (5 lessons)
- Level
- Foundation
- Price
- Free
- Certificate
- On completion
Where every evaluator begins.
Before you score a single AI output, Foundation gives you the shared language and standards the rest of the pathway is built on — what large language models actually do, how RLHF turns expert judgement into safer models, and what separates a high-quality evaluation from a careless one. Finish it and you're ready for the two-phase calibration.
What you’ll learn.
What large language models are, how they generate text, and why human feedback matters.
Task interfaces, rating mechanics, and what constitutes quality feedback.
Structured rubric evaluation, dimensional scoring, anchoring, and consistency.
Writing rationales with structured citations, clinical reasoning, and evidence grounding.
Task assignment, payment, turnaround expectations, and quality metrics.
Guided exercises across core task types with immediate feedback.
The modules.
7 modules · 180 minutes · work through them at your own pace.
- 01
Understanding AI & LLMs
What large language models are, how they generate text, and why human feedback matters.
Lesson30m - 02
The RLHF Workflow
Task interfaces, rating mechanics, and what constitutes quality feedback.
Lesson30m - 03
Quality Standards & Rubrics
Structured rubric evaluation, dimensional scoring, anchoring, and consistency.
Lesson30m - 04
Justification Writing
Writing rationales with structured citations, clinical reasoning, and evidence grounding.
Lesson30m - 05
Platform Mechanics
Task assignment, payment, turnaround expectations, and quality metrics.
Lesson15m - 06
Instruction Comprehension Check
Verify understanding of key concepts before proceeding to practice.
Quiz5m - 07
Practice Tasks
Guided exercises across core task types with immediate feedback.
Practice45m
A look inside.
Understanding AI & Large Language Models Learning outcomes By the end of this module, learners will be able to: 1. Explain how a Large Language Model generates text via token prediction, and describe the distinction between statistically-fluent output and factually-correct output.
Register free to access the full course content.
The rest of the pathway.
Ready to begin?
Register free, complete the course, and move on to the calibration that puts a reliability score on your profile.
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entertheloopClinicians powering AI alignment, training & safety.