AI Model Evaluation Scorecard Template

Minimum 50 representative examples plus 10-15 adversarial cases. For high-stakes applications (medical, legal, financial), aim for 200+ examples covering every category of input your product handles. The key is coverage of your input distribution, not raw volume.

No. Dimension weights should reflect your product's specific constraints. A real-time chatbot should weight latency at 25-30%. A batch processing pipeline can weight latency at 5% and put more weight on cost and accuracy. Discuss weights with your team before scoring begins.

Re-evaluate quarterly, when a major new model version launches, or when your production metrics show drift from baseline scores. Set automated alerts for [token cost per interaction](/metrics/token-cost-per-interaction) and accuracy metrics to trigger ad-hoc re-evaluations.

Yes. Replace the LLM-specific criteria (hallucination rate, token costs, context window) with ML-appropriate metrics (precision, recall, F1 score, inference time, training cost). The weighted scorecard structure works for any model comparison.

At minimum: the PM (owns weights and final decision), the ML engineer (owns scoring methodology and test harness), and a domain expert (validates output quality). For safety-critical products, add a representative from legal or compliance.