Model Evaluation

Evaluating an AI model is not only about whether it can answer a question. It is also about how well it does so under constraints such as correctness, clarity, safety, speed, and cost.

Typical evaluation dimensions

A good evaluation setup often checks:

• factual accuracy
• relevance to the user ask
• completeness
• groundedness in source content
• consistency across repeated runs
• safety and policy compliance

Example rubric

Dimension	What to assess
Accuracy	Is the answer correct?
Relevance	Does it answer the actual question?
Clarity	Is it understandable and well-structured?
Safety	Does it avoid harmful or unsupported claims?
Efficiency	Does it respond within acceptable latency and cost?

Practical insight

Human evaluation remains essential for nuanced tasks, while automated scoring can help when you need repeatability across large sets of prompts.

Takeaway

Model evaluation should be treated as a continuous discipline, not a one-time checklist. The best-performing system is usually the one that balances quality with speed and cost.