Evaluation Framework
Systematic approach to measuring AI system quality and performance.
Definition
An AI evaluation framework is a systematic approach to measuring the quality, accuracy, and performance of AI systems. This includes defining metrics, creating test datasets, running benchmarks, and establishing processes for ongoing quality monitoring.
Evaluation frameworks are essential because AI quality isn't binary—systems can be better or worse across many dimensions including accuracy, safety, latency, and consistency. Without rigorous evaluation, teams can't know if changes improve or degrade their systems.
Why It Matters
You can't improve what you don't measure. Evaluation frameworks provide the visibility needed to systematically improve AI systems and catch regressions before users do.
For AI teams, investing in evaluation infrastructure pays dividends throughout the development lifecycle.
Examples in Practice
A company's evaluation framework automatically runs 1,000 test cases on every model update, blocking deployment if accuracy drops.
Evaluation metrics reveal that a prompt change improved speed but degraded accuracy for edge cases, informing the rollback decision.
A/B testing within the evaluation framework proves that the new model version improves customer satisfaction by 15%.