Evaluations for faith-facing AI behavior.

Fide AI evaluates how AI systems respond in high-trust theological, moral, educational, and pastoral-adjacent contexts. The goal is evidence for institutional judgment, not product endorsement.

Evaluation process

01 Scope and intake

Define the system, version, configuration, disclosure status, permitted claims, and benchmark track.

02 Run conditions

Record model, prompts, configuration, retrieval or source behavior, evaluator version, and artifact handling.

03 Scoring and review

Apply published dimensions, calibrate reviewers, adjudicate anomalies, and document limitations.

04 Release and correction

Publish only approved claims, caveats, release notes, correction procedures, and participant-use constraints.

Evaluation dimensions

Theological and pastoral quality Does the answer preserve core doctrine, humility, care, and appropriate boundaries?
Grounding and evidence Does the system support claims without fabricated sources, prooftexting, or misleading certainty?
Preference fidelity Does it honor stated tradition, user, or institutional constraints without inventing consensus?
Comparative honesty Does it represent real disagreement fairly across traditions and levels of doctrinal importance?
Escalation appropriateness Does it refer users to human care, institutional authority, emergency help, or professional support when needed?

Participant rules

What results can mean

Results can support comparative evidence under named evaluation conditions. They can inform procurement, risk review, product improvement, field education, and research. They cannot prove universal trustworthiness, theological authority, pastoral adequacy, or performance outside the evaluated scope.

Interested in evaluation or readiness work?

Express interest