Databricks(@databricks):Classifying text against taxonomies with 100,000+ labels creates a hard tradeoff between accuracy, cost, and maintainability. We tested three approaches across vendor normalization, company deduplication, and biomedical entity linking: • Vector search • Vector search followed by AI Classify • Direct frontier model calls with prompt caching The AI Classify workflow delivered five points higher average accuracy than the next-best direct frontier model at roughly one-hundredth of the per-document cost. The pattern is simple: retrieve the most relevant labels first, then classify. Explore the benchmark and workflow:

2026.08.12 18:50

Classifying text against taxonomies with 100,000+ labels creates a hard tradeoff between accuracy, cost, and maintainability. We tested three approaches across vendor normalization, company deduplication, and biomedical entity linking: • Vector search • Vector search followed by AI Classify • Direct frontier model calls with prompt caching The AI Classify workflow delivered five points higher average accuracy than the next-best direct frontier model at roughly one-hundredth of the per-document cost. The pattern is simple: retrieve the most relevant labels first, then classify. Explore the benchmark and workflow: