Skip to main content
AI Judges evaluate model outputs quickly and consistently, returning a score + rationale for each task. In health and life-science settings, judges help you measure clinical quality, safety, reasoning, citation use, and policy adherence at scale. Human reviewers remain the source of truth, but AI Judges make it practical to evaluate thousands of conversations or documents, highlight mistakes, and focus expert time where it matters most. High-level metrics like accuracy, safety, or reasoning provide trendlines but rarely point to the exact fix. For example, a model that appears to fail safety may actually be missing medication allergy handling — often because it performed weak follow-ups and failed to build a complete clinical picture. Aggregates hide specific, fixable gaps. Traditional RLHF compounds this: most projects track 4-6 metrics because human evaluation is expensive and slow, rubric enforcement is difficult, and asking raters to score 50-60 dimensions both spikes cost and introduces more human error. AI Judges help you expand metric coverage and make the gaps actionable without exploding cost or latency.

​
What are AI Judges?

AI Judges are LLM evaluators that apply task-specific rubrics to model outputs. Each judgment includes:
  • Structured scores (1-4), plus sub-scores by rubric dimension.
  • Rationale - a short, evidence-backed explanation of why the score was assigned.
  • Tags - error type, severity, specialty, guideline references, and confidence.
Judges are calibrated against human experts and checked regularly for reliability, leakage, and bias.

​
Why they matter

  • Speed and coverage - evaluate 100x more samples than human-only workflows.
  • Consistency - a stable rubric applied the same way across time and teams.
  • Actionable insights - rationales surface specific failures that map to clear fixes in prompts, tools, retrieval, or Precise SFT.
  • Auditability - every score links to criteria, evidence, and provenance for compliance.

​
How it works

  1. Human review first
    Domain experts review a sample of your model or agent outputs and identify concrete mistakes that matter in practice.
  2. Map to judges
    We align those mistakes to existing AI Judges. If needed, we create new judges, and we add separate binary flags for the most common failure modes to ensure crisp detection.
  3. Deploy the judges
    Judges are prompted with instructions + rubric + task input + model output. They must:
    • Assign scores per dimension.
    • Provide a concise rationale citing snippets or guidelines.
    • Flag unsafe content and abstain when uncertain.
    • Output a strict JSON schema for downstream processing.
  4. Calibration loop
    • Run a gold set with human consensus labels (2+1 or 3 reviewers).
    • Measure agreement (κ or Krippendorff’s α) by dimension and specialty.
    • Tweak rubric anchors and judge prompts until agreement passes thresholds (e.g., κ ≥ 0.6).
  5. Human alignment
    We use judged data to drive alignment work - prompt and tool changes or Precise SFT that targets the specific errors uncovered by rationales.
  6. Scale with safeguards
    Once accuracy is good enough, humans step back and you use the Evaluator API for continuous scoring. We maintain ongoing spot checks to detect drift and preserve trust.

​
What judges do you have

The panel of judges and dimensions is suggested custom to the task, specialty, and known issues. We maintain 500+ judges covering conversational, generation/summarization, voice, agentic, and retrieval/grounding tasks.
You can see an example set deployed for a conversational task — wellness checkup — in our AI Patient demo.

​
What is the price

Pricing scales with the size of each output, the breadth of evaluation, and the share of human review.
  • Output size
    Conversations or documents with many turns/tokens take longer to evaluate. A mental health triage dialog might run 80 turns, while a cardiology check may be 12. We normalize long items into evaluation units so that unusually long outputs may count as multiple units.
  • Dimensions
    More rubric dimensions mean more judgments per output.
  • Human review share
    You choose what % of judgments are spot-checked by human experts for alignment and drift control.
Baseline pricing for existing, calibrated judges with 20% human alignment starts at $0.05 per dimension per output.
Example
Evaluate 1,000 conversations of ~12 turns each on a core set of 40 dimensions:1,000 outputs x 40 dimensions x $0.05 = $2,000 for 40,000 scores. 
In comparison, if done only with human experts, assuming $150/h and 20 scores per hour, the human pipeline would be about $300,000. Custom judges
  • Adding a new judge to the main panel (calibration in 2+1 style): ~$3,000—$6,000 one-time alignment.
  • Private-only judges (kept exclusive to your deployment): custom proposal — we will scope rubric, gold-set size, and review cadence together.
Notes
  • Very long outputs may be split into multiple evaluation units to keep judgments reliable and turnaround predictable.
  • Higher or lower human-review percentages adjust cost accordingly; we will share a clear quote before any run.

​
Quality controls

  • Adversarial checks - perturb inputs, shuffle choices, hide context to test robustness.
  • Leakage checks - ensure judges do not peek at reference answers that would inflate scores.
  • Bias scans - monitor score drift by population or provider attributes.
  • Confidence gating - low-confidence or abstain cases are escalated to humans.

​
Aggregation and reporting

  • Aggregate per-dimension and overall scores with deterministic formulas.
  • Produce dashboards by task, specialty, error type, and severity.
  • Emit rationales and tags that directly feed targeted SFT and agent fixes.

​
What we’ve observed

  • High triage value - judges reliably identify obvious failures and unsafe advice, letting humans focus on borderline or specialty-heavy cases.
  • Stable over time - with a monthly calibration pass, drift stays minimal and agreement with humans remains in band.
  • Great for iteration - judge rationales translate into clear remediation actions and measurable improvements.

​
What you get

  • Judge rubric pack - criteria, anchors, examples, JSON schema.
  • Calibrated judge prompts - per task and specialty.
  • Gold set and calibration report — agreement metrics, drift baselines, and thresholds.
  • Evaluator service - API to score conversations or documents and return score — rationale — tags.
  • Ops playbook - guidance on sampling, escalation, and monthly maintenance.

​
When to use AI Judges

  • You need continuous evaluation across many specialties or workflows.
  • You are iterating prompts, tools, or agents and need rapid A/B reads.
  • You plan a targeted SFT program and need structured failure mining.
  • You require audit trails for compliance and stakeholder review.

​
FAQs

Do AI Judges replace human experts?
No. They triage and scale evaluation. Humans set rubrics, calibrate judges, and review edge cases and high-risk outputs. How do you prevent judge hallucinations?
Tight prompts, JSON schemas, evidence requirements, and abstain rules. Low-confidence or unsupported rationales are auto-escalated. Can judges cite guidelines?
Yes. Judges can be prompted to require source snippets and guideline identifiers. When a reliable source is missing, judges must abstain or mark low confidence. What about privacy and PHI?
All evaluations run on de-identified data and within compliant environments. We store minimal metadata — enough for auditability, not re-identification. Will judges be biased toward certain styles?
Rubrics reward content over style and include counter-examples. We audit for population and provider bias and rebalance anchors as needed.