Free video · E08
Evaluation: Golden Sets and LLM-as-judge
Chinese narration · Chinese captions · 15:45 · Video published on
This series uses AI-assisted voice narration; rely on the page disclosures and text curriculum for the content boundary. Examples are teaching scenarios, not evidence of customer outcomes.
Episode summary
Start with a prompt change that may alter historical ticket decisions. Learn evaluation datasets, expected criteria and prohibited outputs; distinguish software tests from model evaluation and examine Golden Set layers, judge bias and human review.
- Record correctness, hallucinations, format, cost and latency separately so a total score does not conceal prohibited failures.
- Cover typical, edge and historical failure cases while preserving versions and a baseline. Sample counts and ratios in the video are teaching starting points, not universal quality thresholds.
- Model judges need explicit rubrics, human spot checks and bias checks. Reordering answers or changing judges cannot guarantee unbiased results; verify rule-based and model evidence separately.
This is an editorial learning summary, not a transcript. Check the text lessons for conceptual boundaries.
Practice after watching
Prepare synthetic cases with inputs, expected criteria, prohibitions and decision evidence. Label synthetic analysis explicitly and leave actual model outputs and scores for real runs.
Open the exercise, example and reference →