golden-datasetlisted

# Golden Dataset **Curate, manage, and validate high-quality test datasets for AI/ML systems** ## Overview A **golden dataset** is a curated collection of high-quality examples used for regression testing, retrieval evaluation, model benchmarking, and reproducibility. This skill covers the full lifecycle: curating new entries with quality analysis, managing backup/restore operations, and validating data integrity. ### Example Golden Dataset Metrics | Metric | Value | |--------|-------| | Documents | 98 completed | | Chunks | 415 embedded segments | | Test queries | 203 with expected results | | Pass rate | 91.6% retrieval quality | **Purpose:** Test hybrid search (vector + BM25 + RRF), validate metadata boosting, detect retrieval regressions, benchmark embedding models. --- ## Curation Quality criteria, workflows, and multi-agent analysis patterns for evaluating and adding documents to the golden dataset. **Key areas:** - **Content type classification** -- article, tutorial, research paper, documentation, video transcript, code repository - **Difficulty stratification** -- trivial, easy, medium, hard, adversarial (based on semantic complexity) - **Quality dimensions** -- accuracy (0.25), coherence (0.20), depth (0.25), relevance (0.30) - **Multi-agent pipeline** -- parallel evaluation with Quality Evaluator, Difficulty Classifier, Domain Tagger, Query Generator - **Duplicate prevention** -- URL check + semantic similarity > 80% threshold **Detailed patterns:** [ref