AI Analysis #2: Model Performance vs. Real-World Use

Why benchmarks can mislead

Benchmarks are useful, but they don’t always predict how a model behaves in real workflows. Here’s how I evaluate results beyond headline scores.

My evaluation checklist

• Data leakage and test contamination risks

• Robustness across prompts, domains, and edge cases

• Latency, cost, and reliability under load

• Safety and failure modes (hallucinations, bias, privacy)

Practical recommendation

Before adopting any model, run a small pilot with your real tasks and measure outcomes that matter to your users.