Evaluation
The systematic process of measuring an AI model's performance, safety, and reliability using various metrics, benchmarks, and testing methodologies.
Why It Matters
Rigorous evaluation prevents deploying models that seem good in demos but fail in production. It is the quality control step that separates toys from tools.
Example
Running an LLM through automated benchmarks for accuracy, human evaluation for helpfulness and safety, and adversarial testing for robustness before release.
Think of it like...
Like quality assurance testing for software — you do not ship a product just because it works sometimes, you need systematic verification that it works reliably.
Related Terms
Benchmark
A standardized test or dataset used to evaluate and compare the performance of AI models. Benchmarks provide consistent metrics that allow fair comparisons between different approaches.
Accuracy
The percentage of correct predictions out of all predictions made by a model. While intuitive, accuracy can be misleading for imbalanced datasets.
Precision
Of all the items the model predicted as positive, the proportion that were actually positive. Precision measures how trustworthy the model's positive predictions are.
Recall
Of all the actually positive items in the dataset, the proportion that the model correctly identified. Recall measures how completely the model finds all relevant items.
F1 Score
The harmonic mean of precision and recall, providing a single metric that balances both. F1 scores range from 0 to 1, with 1 being perfect precision and recall.
Human Evaluation
Using human judges to assess AI model quality on subjective dimensions like helpfulness, coherence, creativity, and safety that automated metrics cannot fully capture.