model-evaluation

Covers the practice of measuring how AI models actually perform, beyond headline benchmark scores. Content here digs into calibration, robustness, reasoning faithfulness, and the gap between what a metric reports and what a model can be trusted to do in production. Expect discussion of evaluation design, the limits of standard benchmarks, and why properties that make a model reliable often escape the numbers used to rank it.

Glowing head dissolving into particles that rebuild a hollow empty copy, showing what knowledge distillation loses.

knowledge-distillation, tacit-knowledge, model-evaluation, ai-expertise, publishing-expertise

What Actually Gets Distilled: Copying the Method Is Not Copying the Judgment

Knowledge distillation reliably copies a model's outputs and benchmark scores while losing the properties that made them trustworthy: calibration, robustness, and reasoning faithfulness. The same split applies to human expertise absorbed by AI systems -- your explicit method transfers cleanly, but y