What Is Model Drift in AI? Types, Causes, and How to Detect It
Model drift is when a model's performance degrades after deployment as the world it learned from changes. Types, causes and how to detect it.

Model drift is when a model's performance degrades after deployment as the world it learned from changes. Types, causes and how to detect it.

A guide to reading LLM benchmark leaderboards: methodology, what's held constant, and how to compare models without being misled by rankings alone.

An AI agent context window fills fast when a tool reads a whole dataset. How our harness keeps large tables on disk and passes paths and summaries instead.

What an AI evaluation framework includes (datasets, rubrics, judges, metrics, reporting) and how the pieces fit into one reliable system.

In a source-grounded benchmark, extracted text is the evidence. How pdftotext modes, OCR damage and pseudonymisation quietly break evidence anchors.

What ground truth data is, how it's created and validated, and why extraction and labeling choices quietly become benchmark choices.

AI model evaluation explained: four methods, how to choose metrics, and how to read results, with numbers from grading eight models on 150 questions.

Benchmark statistical significance in practice: confidence intervals, minimum detectable gaps, and a fairness check on a 50-question AI benchmark.

AI model evaluation with an LLM judge: the judge returns a JSON block the harness checks against the rubric, and an unscored answer beats a guessed one.

Asking the judge twice with answers swapped exposes position bias. The scoring table, worked examples, and why pairwise verdicts can't build a leaderboard.