RLHF Data Providers Compared: Choosing Human Evaluation for Your AI
A neutral guide to the kinds of RLHF and human-evaluation providers, what separates generalist crowds from expert review, and how to choose.
TL;DR. RLHF and human-evaluation providers range from large generalist crowds to small networks of credentialed domain experts. Generalist crowds are fine for tone, formatting and broad preference data; expert review is essential when a wrong answer is domain-specific and dangerous. Choose on reviewer credentials, inter-rater agreement and how errors are scored.
What RLHF and human evaluation actually are
Reinforcement learning from human feedback (InstructGPT, 2022) uses human judgements of model outputs to align an AI's behaviour. Human evaluation more broadly means qualified people rating accuracy, safety and domain-correctness, often to build preference data or to red-team a model before deployment.
The core divide: generalist crowds vs expert review
- Generalist crowds. Large, fast, inexpensive. Good for "is this answer fluent, on-topic and helpful". Weak where correctness needs a professional - a confident, wrong answer about bearing failure modes, drug interactions or contract law sails straight through.
- Expert review networks. Smaller, credentialed reviewers matched to a sub-domain. Slower and dearer per item, but they catch the failures that actually create liability. nxted Expert is in this category.
What to measure in any RLHF provider
- Reviewer credentials. Are they disclosed, and matched to your domain?
- Inter-rater agreement. Reported per project, so you can tell signal from noise?
- Error taxonomy. Are errors classified by type and severity tied to deployment risk?
- Transparency. Do you learn who reviewed your model, or is it a black box?
- Compliance. A signed DPA and, for high-risk systems, documentation that maps to the EU AI Act (Article 14 human oversight, Annex IV).
When you need expert review specifically
If your AI makes decisions a professional would be liable for - clinical, legal, financial, structural - your evaluators should hold the credentials that professional holds. Generalist preference data will not surface the errors that matter, and may hide them behind high agreement on the wrong answer.
Where nxted Expert fits
nxted Expert supplies credentialed domain reviewers across engineering, the sciences, medicine, law and finance, reports inter-rater agreement and an error taxonomy, and ships documentation built to drop into an EU AI Act technical file. See nxted Expert.
FAQ
What is an RLHF data provider? A company that supplies human judgements of AI outputs - preference data, evaluations or red-teaming - to align or assess a model. They range from generalist crowds to credentialed expert networks.
When should I use expert review instead of a crowd? When correctness is domain-specific and a wrong answer creates real risk. Experts catch failures a generalist crowd rates as acceptable.
What should an RLHF report include? Reviewer credentials, inter-rater agreement, an error taxonomy tied to deployment risk, and a signed DPA - ideally mapped to EU AI Act documentation for high-risk systems.
Try expert evaluation on your model: start a free Expert Test Kit or read about nxted Expert.
Physical-AI data specialists at OFORO LTD (UK). We write about egocentric data, robotics dataset formats, RLHF and data governance. See what we build.