Unverified50% confidenceSolutionExact time
A 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.
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Long-term
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10/28/2026
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UnverifiedA 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.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.79% similarUnverifiedThe recommended approach is to use third-party evaluations as a first-pass filter to narrow down candidate models to 2–3 options, then conduct small-scale A/B testing with one's own real business dataset.74% similarUnverifiedIt is recommended to first run small-scale benchmark tests on candidate models using a small number of real samples, record quality, cost, and latency, and make model selection decisions based on data rather than reputation.74% similarUnverifiedTest Selection technology dynamically selects relevant cases to execute based on the scope of code changes, which can further compress unnecessary model calls.74% similar
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