### 5.1 Performance Posture The **epoch_ai_benchmarks** table (1,311 rows) currently operates without specialized indexing for its two most query-intensive text fields. The `Notes` column—likely containing detailed benchmark methodology, caveats, or contextual explanations—and `original_task_name` field are accessed frequently but stored as plain text without search optimization. At current scale this poses minimal latency risk, but as the benchmark catalog grows or analytical queries become more complex (filtering by task characteristics, searching methodology notes), response times will degrade linearly without intervention. ### 5.2 Key Optimizations | Target | Optimization Type | Recommendation | Strength | |--------|-------------------|----------------|----------| | epoch_ai_benchmarks.Notes | Text Search | tokenized_text | High | | epoch_ai_benchmarks.original_task_name | Text Search | keyword | High | Implementing tokenized full-text search on `Notes` will accelerate queries that filter benchmarks by methodology keywords (e.g., "few-shot," "zero-shot," "human baseline"), enabling analysts to rapidly segment the catalog by evaluation approach. Keyword indexing on `original_task_name` will speed exact-match lookups and prefix searches—critical when stakeholders need to track specific benchmarks (MMLU, HellaSwag, etc.) across model releases or compare performance on standardized tasks. Together, these optimizations will reduce query latency by an estimated 60–80% for text-heavy filters, directly improving the responsiveness of benchmark comparison dashboards and research tooling without requiring schema changes or data migration.