Service
RLHF Annotation
We build human preference datasets and evaluation pipelines that help LLMs become more accurate, helpful, and aligned.
Section 01
Use Cases
Applied
- Pairwise preference ranking for response quality
- Safety and policy compliance evaluation
- Domain-specific assistant fine-tuning
- Model benchmarking and red-team feedback
Section 02
Deliverables
Output
- Pairwise and listwise ranked outputs
- Rubric-based quality scores
- Safety and toxicity labels
- Reviewer rationale and adjudicated samples
Section 03
Process
- 1Rubric and policy calibration
- 2Reviewer training and pilot rounds
- 3Scaled annotation with disagreement resolution
- 4Dataset packaging for alignment workflows