EVALUATION & RELEASE
How to evaluate AI behavior changes
A controlled evaluation model for comparing exact candidate and baseline behavior before release.
DefinitionEvaluating an AI behavior change means comparing an exact candidate against an exact baseline on defined cases and risk criteria, then sealing the evidence used for the release decision.
KEY TAKEAWAYS
- Evaluation should compare an exact candidate against an exact baseline.
- Critical regressions can block release even when aggregate quality improves.
- The evidence used for release should be sealed before promotion.
Start with identity, not a vague before-and-after
An evaluation is only useful if the candidate and baseline are identifiable. The behavior revision, dataset or case set, model target, and relevant execution settings should be explicit.
Without exact identity, a passing result can accidentally refer to a different prompt, model, revision, or scope composition than the one later released.
Separate ordinary quality from critical regressions
Aggregate scores can hide failures that matter disproportionately. Policy weakening, forbidden actions, missing escalation, or broken verification may deserve release-blocking treatment even when an average score improves.
A useful evaluation surface therefore shows both overall comparison and critical-regression evidence instead of reducing the release decision to one green number.
Seal evaluation evidence before release
Once a candidate is considered release-ready, the evidence used for that decision should become immutable. The release process can then verify the candidate revision and artifact identity against that sealed evaluation.
This separates offline evaluation authority from online promotion authority. Passing an evaluation does not itself move production traffic.
Treat automated judges as evidence, not unquestionable truth
Model-assisted evaluation can be useful, but high-impact decisions may also require deterministic checks, human review, or both. The evaluation method should match the risk of the behavior being changed.
Vira's public material intentionally avoids claiming a universal evaluation recipe. The correct case mix and evidence threshold depend on the application and the failure being governed.
SOURCE & LIMITATION NOTE
Product and Docs links support Vira architecture descriptions. External references are labeled separately. None of them should be read as benchmark evidence unless a dated methodology and result set is published.
REFERENCES