Dataset engineering for model training: where to start
A pillar guide to data for training and evaluating AI: golden records, golden editors, train/eval splits, leakage, manifests, and parameters by task and domain.
Stuzhuk Lab — Chemistry of Code
Chemistry of Code
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evaluation
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Language Model Quality Testing: What the Tests Are and What They Actually Measure
LLM quality checks: public task sets, arenas, model judges, safety, product regression, and online signals — what each test measures and what it cannot see.
AI Evaluation Harness in 2026: Golden Sets, Regression Gates, and Release Criteria That Survive Production
Build an AI evaluation harness that connects golden datasets, retrieval and generation metrics, CI regression gates, shadow traffic, release controls, observability, and LLM gateway FinOps.
Evaluating Enterprise AI: Metrics You Can Trust
A practical framework for measuring enterprise AI across retrieval, answers, security, and business outcomes.