RLHF Operations Playbook: From Prompt Queue to Accepted Labels
Most RLHF programs fail in operations, not in prompt design. Teams can create solid rubrics but still miss throughput targets because queue shaping, reviewer calibration, and escalation criteria are under-specified.
A stable RLHF workflow starts by separating objective checks from subjective preference calls. Objective checks should be automated first, while preference decisions route to calibrated reviewers with strict disagreement handling rules.
Core operating loop
Step one is task normalization: convert incoming prompts into taxonomy-tagged units with known complexity bands. Step two is capability routing: assign higher-complexity units only to reviewers with proven agreement quality in that domain.
Step three is dual review with adjudication on disagreement. Step four is error clustering: rejection reasons are grouped and fed back into instruction updates and reviewer retraining.
Metrics that matter
Track acceptance rate, adjudication frequency, and cost per accepted label together. Acceptance alone can be gamed by lower standards; cost alone can hide quality debt.
The right target is stable quality under SLA, with rejection causes trending down across weekly snapshots.