Preference Ranking
Human reviewers compare AI responses and select the better output based on defined quality criteria.
// rlhf
RLHF and human feedback operations.
Human feedback remains one of the most important inputs for improving AI model behaviour. We operate managed RLHF workflows designed for consistency, quality, and scale.
// feedback_services
End-to-end RLHF support.
Rubric-Based Scoring
Evaluators score outputs across dimensions such as accuracy, helpfulness, tone, safety, reasoning, and instruction-following.
Alignment Feedback
Reviewers identify behaviours that deviate from expected model standards, safety policies, or product requirements.
Dataset Review
We help review, clean, and validate human feedback datasets before they are used in training or evaluation.
Calibration Loops
Reviewer performance is measured against gold-standard tasks and reviewed continuously to reduce drift.
// workflow
How we operate RLHF workflows.
01
Task Design
Define comparison criteria, ranking dimensions, and edge case handling.
02
Reviewer Training
Calibrate reviewers with sample comparisons and gold-standard examples.
03
Preference Collection
Execute preference ranking with tracked quality and consistency metrics.
04
QA & Delivery
Validate outputs, resolve disagreements, and deliver formatted datasets.
// use_cases
Common RLHF applications.
[01]Response quality comparison for chat models
[02]Helpfulness and harmlessness ranking
[03]Instruction-following preference data
[04]Safety alignment feedback collection
[05]Multi-turn conversation evaluation
[06]Code generation preference ranking
Need RLHF data collection at scale?
Start a pilot to see how we can support your human feedback workflows.
[Request Pilot]