15 March 2026
How I Built Clinical-Grade AI Validation at Reggie Health
In two months, we replaced a hallucinating prototype with a deterministic, clinical-grade system. Here's how we did it.
How I Built Clinical-Grade AI Validation at Reggie Health
The Problem
Reggie Health had built a voice AI agent for clinical documentation. It worked - sometimes. But in healthcare, “sometimes” means “never.” When the AI hallucinated a medication dosage or missed a critical symptom, the liability was catastrophic.
The Solution
Over eight weeks, I built a three-layer validation system:
Layer 1: Input Guardrails
- Structured prompts with constrained output schemas
- Context boundary enforcement
- PII detection and redaction
Layer 2: LLM-as-Judge
- Secondary model evaluating primary outputs
- Deterministic scoring rubrics
- Confidence thresholds with human escalation
Layer 3: Audit Trail
- Complete request/response logging
- Version control for prompts
- Automated drift detection
The Result
The founder could finally ship. The system went from “demo-worthy but dangerous” to “production-ready and compliant.”
Key Insight
Individual AI creates chaos. Institutional AI creates coordination. The validation layer is what turns “smart” into “safe.”