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

说明与用途

在升级LLM模型时，团队往往仅凭基准得分更换模型ID，忽视了准确率、延迟和令牌成本的变化。PromptProof 自动评估这三个维度，帮助团队基于证据做出决策，而非凭直觉。

问题

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.

结果

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

工作流

• 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.

证据

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

来源

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

来源

This is the workflow PromptProof exists for.

来源