When the judge shares the blind spot Data Geoff RJune 15, 2026June 15, 202615 mins0 LLM-as-judge blind spots are the systematic reasoning failures an automated evaluator inherits from the model it is…continue reading..
Evaluator-backed benchmarking: your benchmark is only as good as your evaluators Data Geoff RJune 9, 2026June 9, 202613 mins0 Evaluator-backed benchmarking is the structural counter to benchmark gaming. When the underlying evaluators carry verifiable identity, longitudinal…continue reading..
Continuous training needs continuous evaluators Data Geoff RJune 4, 2026June 4, 202614 mins0 Longitudinal evaluation is the human-judgement layer that scales alongside continual model adaptation. A continually retrained model paired…continue reading..
When benchmarks break: the case for traceable evaluator provenance Data Geoff RJune 1, 2026June 1, 202611 mins0 Evaluator provenance is the layer that turns benchmark results from “trust the publisher” claims into independently verifiable…continue reading..
Reputation as public infrastructure Data Geoff RMay 22, 2026May 22, 202611 mins0 The supply of trusted AI evaluators is bottlenecked not by a shortage of humans but by platform-bound…continue reading..
Why persistent identity is the missing layer under AI evaluation Data Geoff RMay 19, 2026May 19, 202611 mins0 Model drift in flagship AI systems is often misattributed to changes in the model when it is,…continue reading..
