How do we currently evaluate whether an AI model is performing well for our use case?

If evaluation is 'it seems to work,' you have no baseline and no way to detect degradation. Use a representative test set of your actual queries scored against defined quality criteria.

Do we have a representative test set of our own data to benchmark models against?

A test set from your actual workload is more valuable than any public benchmark. Collect 50-100 representative queries with known-good answers and run every candidate against them.

When was the last time we compared our current AI model's performance against alternatives?

Models improve and prices drop constantly. A model that was best six months ago may now be outperformed by a cheaper alternative. Re-evaluate quarterly.

The AI Industry

Model benchmarking & evaluation

By Mark Ziler · Last updated 2026-04-05

Benchmarking is how the industry measures whether one AI model is better than another at specific tasks — coding, math, reasoning, following instructions. For your business, the headline benchmark scores matter less than real-world performance on your actual use case. A model that scores highest on a coding benchmark might not be the best at analyzing your service data. Always test with your own scenarios before committing.

Go deeper

A vendor pitches you their AI solution and shows you a benchmark chart where their model scores 94% on reasoning tasks. Sounds impressive. But your actual use case is extracting procedure codes from messy clinical notes written by 47 different providers with inconsistent formatting. That generic benchmark tells you nothing about how the model handles abbreviations your providers invented, crossed-out-and-rewritten entries, or notes that reference 'the same thing we discussed last time' without specifying what that was.

The trap most companies fall into is letting vendors define the test. They'll always demo their strongest scenario. Instead, bring your own messiest, most representative real-world examples — the ones that trip up your current staff — and ask the vendor to run those through their system while you watch. The model that handles your worst-case data well will handle your normal data easily.

Questions to ask

Do we have a set of 20-30 real-world test cases from our own operations that we use to evaluate every AI tool, or do we let vendors cherry-pick their demo scenarios?
When a vendor shows benchmark scores, do we ask which benchmarks and how they relate to our specific workflows?
Have we tested the same use case on multiple models to see how much performance actually varies?