Unverified70% confidenceSolutionExact time
A tiered evaluation strategy uses small models or rules for initial filtering, deploys expensive large-model judges only at critical stages, uses caching to avoid redundant evaluations, and uses refined evaluation sets to reduce token consumption.
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70%
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Long-term
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8/31/2026
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UnverifiedA common practice is to first run offline comparisons with a small-scale evaluation set, then conduct A/B canary releases on candidate models, while retaining multi-model routing capability to avoid lock-in to a single vendor.79% similarUnverifiedIn practice, common ways to reduce reliance on a single model's judgment are: keeping a fixed-version judge model, combining it with manual spot checks, and maintaining rule-based tests with clear decision criteria for key scenarios.77% similarUnverifiedA policy ladder is recommended: at the bottom level, approval is only for evaluation (not counted toward required approvals), at the next level it may count as approval only on explicitly low-risk paths, and at the level above that, policy evidence (explainable approval) is required.75% similarUnverifiedWhen making a ruling, the policy layer/gateway checks identity verification, remaining budget, rate limits, and model policies, and outputs either allow or deny.75% similarUnverifiedRetrieval and generation must be tested separately; by building a small evaluation set, first check separately whether the retriever recalls the correct evidence, and then evaluate the model's wording.75% similar
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