Bootstrapping RLHF Programs for Enterprise AI

As enterprises move from experimental LLM wrappers to fine-tuned proprietary models, the bottleneck shifts from compute to data quality. Specifically, Reinforcement Learning from Human Feedback (RLHF) has emerged as the critical differentiator in model performance and safety. But building a scalable RLHF program is operationally complex.

The Quality vs. Scale Dilemma

Crowdsourced annotation platforms offer scale but often suffer from poor quality control and lack of domain expertise. For enterprise applications—legal, medical, financial—you cannot rely on generic labelers. You need experts.

Neumog is pioneering the 'Expert Pod' model for RLHF. We assemble small, highly qualified teams of domain experts (e.g., paralegals, junior doctors, financial analysts) managed by a central Data Quality Lead.

Operationalizing the Feedback Loop

Launching an RLHF initiative requires more than just people; it requires a robust operational framework.

• Guideline Iteration: We treat annotation guidelines as a living product, iterating daily based on edge cases.
• Inter-Annotator Agreement (IAA): Rigorous statistical monitoring to ensure consistency across the pod.
• Golden Sets: Continuous testing against ground-truth data to prevent drift.

"Data is the new code. The quality of your human feedback loop directly dictates the quality of your model's inference."

For a recent LegalTech client, our expert pod improved model accuracy on contract review tasks by 22% in just six weeks. By bootstrapping a high-fidelity RLHF loop, we helped them achieve regulatory compliance ahead of schedule.

Scale with confidence. See how RLHF fits your roadmap at neumog.tech.