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

Description and use

When upgrading LLM models, teams often swap the model ID based on benchmark scores, overlooking changes in accuracy, latency, and token cost. PromptProof automates evaluation of these three dimensions, enabling evidence‑based decisions rather than assumptions.

Problem

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.

Outcome

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

Workflow

• 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.

Evidence

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

Source

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

Source

This is the workflow PromptProof exists for.

Source