Comparison

Prova vs Constitutional AI

Constitutional AI hopes a model trained the right way reasons correctly. Prova proves whether a specific reasoning chain is logically valid, independent of how the model was trained.

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Constitutional AI is a training methodology. It nudges a model toward a principle list at training time and relies on the model to self-critique at inference. Prova is a runtime verification system. It takes any AI reasoning chain, extracts the argument graph, and issues a signed certificate of structural validity. The two approaches solve different problems and are not mutually exclusive.

FeatureProvaConstitutional AI
Works on any model, not just self-critiquing onesYesNo
Formal per-instance verificationYesNo
Detects circular reasoningYessometimes
Detects unsupported leapsYessometimes
Issues signed certificatesYesNo
Immutable audit logYesNo
Requires retraining to change behaviorNoYes
Runtime cost measured in secondsYesNo
Math foundationH1(K;Z) = 0principle list + RLHF
Self-hosted deploymentYesmodel-dependent

Where Prova is different

Works on any model you already use

Constitutional AI only helps if the model was trained with it. Prova verifies reasoning from any model, including closed-source APIs and open-weights checkpoints you did not train yourself.

Per-instance evidence, not population-level hope

Constitutional training improves aggregate behavior. It does not produce evidence that a specific reasoning chain on a specific Tuesday was logically sound. Prova does exactly that, per call.

Certificates are auditable artifacts

Regulators and model-risk reviewers want to see evidence for the decision in front of them. A principle list and a training recipe are not that evidence. A signed certificate is.

Bottom line

Use Constitutional AI to shape model behavior at training time. Use Prova to certify that a specific reasoning chain is logically valid at runtime. The two pair cleanly; they are not substitutes.

Verify reasoning from any model, not just the ones you trained