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Evaluate a model by meaningful data slices
An aggregate score can conceal different behavior across product plans, missing-data conditions or usage patterns. Choose slices that connect to deployment decisions, then report their denominators and limitations alongs
Evaluate an advanced FDE programme from its deliverables
A long syllabus can still produce shallow evidence. For experienced engineers, the better question is what complete delivery artifacts they must build, break, operate and defend.
Evaluate an agent trajectory as well as its final answer
An agent can reach a correct answer after unauthorized, wasteful or fragile steps. Final-answer scoring alone misses unnecessary calls, leaked data and invalid approvals.
Evaluate an analytics course using its assessment evidence
Evaluate an analytics course by examining what learners must produce, how the work is checked and what feedback leads to improvement. A syllabus can list SQL, Python, Power BI and AI without showing how deeply those skil
Evaluate citations in an AI-generated analytical answer
Evaluate an analytical citation by asking whether the source exists, whether the version applies and whether it supports the specific claim. A valid link beside a sentence does not establish that every number or conclusi
Evaluate image models beyond aggregate accuracy
Aggregate accuracy assigns the same weight to every image and error. It can hide a failed minority class, capture device or low-quality slice. A complete image-model report begins with a reconciled confusion matrix and a
Evaluate OCR on difficult document layouts
OCR quality varies with columns, tables, rotation, scans, handwriting, fonts and image degradation. A clean-page average can conceal the layout that breaks the downstream workflow.
Evaluate private inference against a managed model API
Private inference is not automatically safer or cheaper, and a managed API is not automatically operationally simpler after enterprise controls are counted. The choice depends on data boundary, quality, latency, capacity
Evaluate prompt changes on a fixed task set
Testing a new prompt on whichever examples inspired the edit creates selection bias. Freeze representative task IDs and labels first, run every candidate on the same cases and inspect regressions as well as the average.
Evaluate rare-event models with confidence intervals
A rare-event metric can move substantially when a small number of positives change. Reporting only average precision to three decimals hides that sampling variation. The resampling unit must match dependence in the data.
Evaluate recommendations with temporal holdouts
A random interaction split can train on a user's future and test on their past. Recommendation systems serve forward in time, so an offline split should reproduce what the system knew before each held-out interaction.
Evaluate retrieval on numerical tables
Table questions require the right row, column, unit and condition. A response can quote the correct number from the wrong band and still look plausible.
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