Measured improvements.
Selected results from retrieval, structured generation and model evaluation. Each case includes the dataset, comparison and evaluation conditions.
6.2% better search
by combining two methods.
Combining keyword search with meaning-based search put relevant documents higher in the results.
Read the Hybrid Search case →How this was measured
Both methods searched the same 5,183 research documents using the same 100 sampled queries. The score rewards relevant documents appearing near the top of the first ten results. It rose from 71.76 to 76.23 points out of 100: a 6.2% relative improvement. This is a ranking-quality score, not a percentage of correct answers. Combination weights were fitted on separate query folds; results depend on the documents and queries.
Selected benchmark results
| Study | Recorded result | Evaluation scope |
|---|---|---|
| Text-to-SQL · SpiderQwen 2.5 Coder 14B · 100 questions | 84 of 100 answers correct | Generated database queries returned the expected result on sampled questions. |
| Hybrid Search · SciFact100 queries · 5,183 documents | 6.2% better search ranking | Combined keyword and meaning-based search versus keyword search alone. |
| Dense Search · SciFact300 queries · 5,183 documents | 8.6% better search ranking | Upgraded the meaning-based search model. This measures that component separately. |
| Vector Search · SIFT1M1M image descriptors · 200 queries | 99.9% of closest matches recovered | Average share of the ten true closest image matches found by the approximate index. |
| Sports prediction · NFL EloSeparate 2020–2025 test games · 1,693 games | 63.94% of winners predicted correctly | Historical game predictions. The evaluation used different inputs from the live dashboard. |
Controlled application studies
Structured Output 210 / 210 valid
Correcting the generation token budget raised structural validity from 209/210 to 210/210 on the seven-task regression set. All other outputs were unchanged, and fourteen separately generated prompts passed at both budgets. Structural validity and factual correctness are separate measures.
Read the case →Text-to-SQL refinement 6 / 10 → 7 / 10 correct
A general SQL checklist and wider schema coverage improved a separately registered ten-question comparison. Two answers were fixed and one regressed; both arms executed 9/10 statements with zero skipped cases. The grader and test questions were unchanged. The original 84/100 result is a separate baseline, not a score for this refinement.
Read the case →MCP Servers 27 / 27 expected outcomes
Four Python servers and 11 tools were exercised across 24 fixture-based scenarios. All 27 calls matched their expected results, including expected refusals. This evaluates tool contracts.
Read the case →Semantic Caching 14.7% → 78% of reusable answers found
The cache found a correct reusable answer for 117 of 150 matching questions, up from 22 of 150, in a synthetic study. Both settings returned two incorrect answers across 300 total queries. A separate 30-query live replay avoided seven model calls; this does not establish faster response times.
Read the case →Model Evaluation 150 calibration labels
The model judge matched the reference grade on 79.3% of 150 constructed labels. The case includes disagreement analysis and explains what this comparison can establish.
Read the case →Domain evaluation engineering
Recorded contributions to grading-related implementation across four evaluation repositories, supported by reviewed source and Git history. The work adds to the portfolio's focus on structured evaluation, outcome grading and reproducibility.
Contribution scope
The review inventoried eleven Linux evaluation roots. Broader domain attribution requires task-to-grader and contribution linkage before individual benchmark claims are published. Confidential client material, prompts, rubrics and results remain private.
Local evaluations were conducted on October 13, 2026. Structured Output improved to 210/210 valid completions. Each case identifies its model, dataset, comparison and verification scope.