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How Rubric Compares

Rubric occupies a unique position in the AI evaluation landscape. While LangSmith provides general-purpose LLM observability, and platforms like Mercor and Micro1 have pioneered AI-powered evaluation in recruiting, Rubric brings domain-specific rigor to evaluating healthcare AI systems.

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Platform Comparison

AspectRubricLangSmithMercor / Micro1
What’s evaluatedHealthcare AI outputsAny LLM applicationHuman job candidates
Domain focusClinical decision-makingGeneral LLM workflowsTechnical skills & job fit
Evaluation methodClinical scoring + physician reviewLLM-as-judge + custom evaluatorsAI interviews & skill assessments
Key metricsTriage accuracy, red flag detection, guideline complianceCoherence, correctness, latency, costCoding ability, soft skills, language proficiency
Human reviewLicensed clinicians grade AI decisionsGeneral feedback from reviewersEmployers review AI-screened candidates
ComplianceHIPAA, FDA SaMD, clinical protocolsSOC 2, general data privacyHiring laws, bias mitigation
Specialized forPatient safety, clinical workflowsDeveloper debugging, prompt iterationTalent matching, hiring velocity

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Rubric vs. LangSmith

LangSmith is excellent for general LLM observability — tracing agent behavior, debugging prompt chains, and running evaluations with LLM-as-judge patterns. But healthcare AI requires more:
LangSmith (General LLM Eval)Rubric (Healthcare AI Eval)
Generic evaluators: coherence, relevance, correctnessClinical evaluators: triage accuracy, red flag detection, guideline compliance
Any reviewer can provide feedbackOnly licensed physicians/nurses can grade clinical decisions
Prompt versioning and A/B testingProtocol versioning with clinical validation requirements
Cost and latency monitoringSafety-weighted scoring (under-triage penalized more than over-triage)
SOC 2 complianceHIPAA + FDA SaMD + clinical audit trails
Debug why your agent failedDebug why your AI missed a heart attack
LangSmith asks: “Is this output coherent and helpful?”
Rubric asks: “Is this output clinically safe and protocol-compliant?”

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Rubric vs. Mercor / Micro1

Mercor and Micro1 demonstrated that AI can effectively evaluate at scale — conducting thousands of interviews daily, assessing skills, and matching candidates to roles. They’ve proven that:
  • AI can standardize evaluation — Consistent criteria across every assessment
  • Scale doesn’t sacrifice quality — High-volume screening with reliable signals
  • Human review enhances AI — Combining automated screening with expert judgment
Rubric applies these same principles to a different challenge: evaluating AI systems that make clinical decisions. Just as Mercor’s AI interviews assess whether a candidate can handle a job, Rubric’s evaluators assess whether your healthcare AI can handle patient care safely.
Recruiting AI EvaluationHealthcare AI Evaluation
”Did the candidate correctly solve the coding problem?""Did the AI correctly triage the patient?"
"Can they communicate effectively?""Did it follow clinical communication guidelines?"
"Did they miss any key requirements?""Did it miss any red flag symptoms?"
"Should we advance them to human review?""Should this case go to clinician review?“

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1. Why Healthcare Needs Its Own Platform

General-purpose tools like LangSmith weren’t built for clinical contexts. Recruiting platforms like Mercor weren’t built for AI evaluation. Healthcare AI demands specialized infrastructure:
  • Clinical context matters — A recruiting AI can misrank candidates; a healthcare AI can miss a heart attack. LangSmith can tell you an output was “incoherent” but not that it violated chest pain protocols.
  • Regulatory requirements — HIPAA, FDA SaMD, and clinical validation requirements don’t exist in general LLM tooling or recruiting platforms
  • Expert reviewers — Rubric routes to licensed clinicians with credential verification, not general annotators or hiring managers
  • Safety-first metrics — Under-triage is penalized more heavily than over-triage; this asymmetric weighting doesn’t exist in generic evaluation frameworks
  • Healthcare-native schemas — DICOM metadata, ICD-10 codes, clinical transcripts with speaker diarization — not generic “input/output” pairs
# General LLM evaluation
evaluators = [
    {"type": "relevance"},
    {"type": "coherence"},
    {"type": "hallucination"}
]

# Healthcare-specific evaluation
evaluators = [
    {"type": "triage_accuracy", "config": {"penalize_under_triage": 5.0}},
    {"type": "red_flag_detection", "config": {"protocols": ["chest_pain", "stroke"]}},
    {"type": "guideline_compliance", "config": {"guideline": "schmitt_thompson"}}
]

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2. Compliance Requirements

Healthcare AI evaluation must meet regulatory standards:
RequirementWhy It Matters
HIPAA compliancePatient data protection
Audit trailsRegulatory inspections
Credential verificationOnly qualified reviewers assess clinical decisions
Data residencyPHI must stay in approved regions

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3. Multi-Modal Healthcare Data

Healthcare AI works with specialized data formats:

Voice Triage

Speaker-labeled transcripts, audio quality, call duration

DICOM Imaging

Pixel coordinates, anatomical regions, modality-specific metadata

Clinical Notes

SOAP structure, ICD codes, medication lists

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4. Expert Review Workflows

Healthcare AI review requires clinical expertise: Generic annotation tools:
  • Any user can label data
  • Simple approve/reject workflows
  • No credential requirements
Rubric clinician review:
  • Credential-based task routing (MD, NP, RN)
  • Clinical grading rubrics
  • Audio playback with transcript sync
  • Protocol-specific review criteria

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When to Use What

  • Building patient-facing healthcare AI
  • Evaluating clinical decision-making (triage, diagnosis support)
  • Need HIPAA-compliant evaluation pipeline
  • Require clinician review with credential verification
  • Working with DICOM, medical audio, or clinical notes
  • Must demonstrate regulatory compliance (FDA SaMD)
  • Building non-clinical AI features (appointment scheduling UI, general Q&A)
  • Early prototyping before clinical deployment
  • Internal tools not involving patient data
  • Already have custom healthcare evaluation built
  • You need general LLM observability (Braintrust/LangSmith) for development
  • Plus specialized clinical evaluation (Rubric) for safety-critical features
  • Different teams own different parts of the stack

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What Customers Say

“We tried building healthcare evaluators on top of LangSmith. After 3 months, we had a fraction of what Rubric provides out of the box. The clinician review UI alone saved us 6 months of development.”— VP of Engineering, Digital Health Startup

“Our compliance team required HIPAA-compliant evaluation with audit trails. Rubric was the only platform that met our requirements without extensive custom work.”— Chief Medical Officer, Telehealth Company

“The built-in triage evaluators caught safety issues our generic LLM evals missed. We found 3 under-triage patterns in the first week.”— ML Lead, Healthcare AI Startup

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Migration Path

Already using another platform? Rubric integrates alongside your existing stack: 
# Use both platforms
from langsmith import Client as LangSmith
from rubric import Rubric

langsmith = LangSmith()  # General LLM tracing
rubric = Rubric()        # Clinical evaluation

async def triage_call(audio_url, transcript):
    # Trace with LangSmith
    with langsmith.trace("triage_call"):
        result = await run_triage_model(transcript)
    
    # Evaluate with Rubric
    rubric.calls.log(
        project="patient-triage",
        audio_url=audio_url,
        transcript=transcript,
        ai_decision=result
    )
    
    return result

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Next Steps

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