An LLM Judge That Can't Invent a Score
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.

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.

Calibration fixtures test a rubric before it grades any model. The dominant defect is double credit across the gate: here's what our fixture runs found.

How LLM judges work, single vs. pairwise judging, rubric design, bias sources, and how to validate a judge before trusting its scores.

A one-prompt authoring flow that stops once at the blueprint, runs four machine gates, and resumes from its own files. What it catches, and what it doesn't.

Designing, running and interpreting AI model benchmarks, from question construction to statistical significance, so results actually hold up.

A leaderboard score belongs to a model and a harness together. Here's how pinning the model isolates the harness effect, and where that breaks down.

AI agent evaluation covers reasoning, tool use, trajectories and end-to-end outcomes: how it differs from model evaluation and how to build a framework.

Agents with file and shell tools can reach the grading rubric on disk. What we measured across 50 questions, which fixes failed, and what the harness does now.

Best AI for Geology Testing. See how Groundtruth's dynamic benchmark tests models on your own data, comparing accuracy, cost, and performance.