A New Model Dropped. Don't Just Swap the ID.

Описание и применение

При переходе на новую модель LLM команды часто просто меняют идентификатор модели, полагаясь на результаты публичных бенчмарков. Это приводит к скрытым ухудшениям точности, задержек и стоимости токенов. PromptProof автоматизирует оценку трёх ключевых метрик, позволяя принимать решения на основе статистических доказательств, а не догадок.

Задача

Teams often upgrade LLM models by simply swapping the model ID, assuming the new model is better because of benchmark scores, leading to unnoticed degradations in accuracy, latency, and token cost.

Результат

PromptProof enables rapid, evidence‑based model migration by automating accuracy, latency, and token volume comparisons, reducing process cost and preventing silent degradations.

Рабочий процесс

• 1. Maintain a shared labeled dataset in the product, updated by domain experts.
• 2. When a new model arrives, attach the same prompt to both the old and new models.
• 3. Run a statistical experiment (10–50 trials per item) with a chosen confidence level, executed in the background.
• 4. Collect metrics: accuracy with confidence intervals per field, latency percentiles (p25/p50/p75), and input/output token volumes (median and spread).
• 5. Compare results using the decision rule: no field’s interval drops, latency stays within budget, and token volume is within pricing limits.
• 6. If any dimension regresses, re‑tune the prompt or postpone the upgrade.
• 7. Document and store the experiment results for future migrations.

Доказательства

The new model scores higher on the public benchmarks, so it should score higher on your task too.

Источник

When you do measure it, three things can move, and they can move in opposite directions.

Источник

This is the workflow PromptProof exists for.

Источник