Tursio Database Search: How far are we from ChatGPT?
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912979911442432 |
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| author | Jain, Sulbha Tripathi, Shivani Qiao, Shi Jindal, Alekh |
| author_facet | Jain, Sulbha Tripathi, Shivani Qiao, Shi Jindal, Alekh |
| contents | Business users need to search enterprise databases using natural language, just as they now search the web using ChatGPT or Perplexity. However, existing benchmarks -- designed for open-domain QA or text-to-SQL -- do not evaluate the end-to-end quality of such a search experience. We present an evaluation framework for structured database search that generates realistic banking queries across varying difficulty levels and assesses answer quality using relevance, safety, and conversational metrics via an LLM-as-judge approach. We apply this framework to compare Tursio, a database search platform, against ChatGPT and Perplexity on a credit union banking schema. Our results show that Tursio achieves answer relevancy statistically comparable to both baselines (97.8% vs. 98.1% on simple, 90.0% vs. 100.0% on medium, 89.5% vs. 100.0% on hard questions), even though Tursio answers from a structured database while the baselines generate responses from the open web. We analyze the failure modes, identify database completeness as the primary bottleneck, and outline directions for improving both the evaluation methodology and the systems under evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18835 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tursio Database Search: How far are we from ChatGPT? Jain, Sulbha Tripathi, Shivani Qiao, Shi Jindal, Alekh Databases Business users need to search enterprise databases using natural language, just as they now search the web using ChatGPT or Perplexity. However, existing benchmarks -- designed for open-domain QA or text-to-SQL -- do not evaluate the end-to-end quality of such a search experience. We present an evaluation framework for structured database search that generates realistic banking queries across varying difficulty levels and assesses answer quality using relevance, safety, and conversational metrics via an LLM-as-judge approach. We apply this framework to compare Tursio, a database search platform, against ChatGPT and Perplexity on a credit union banking schema. Our results show that Tursio achieves answer relevancy statistically comparable to both baselines (97.8% vs. 98.1% on simple, 90.0% vs. 100.0% on medium, 89.5% vs. 100.0% on hard questions), even though Tursio answers from a structured database while the baselines generate responses from the open web. We analyze the failure modes, identify database completeness as the primary bottleneck, and outline directions for improving both the evaluation methodology and the systems under evaluation. |
| title | Tursio Database Search: How far are we from ChatGPT? |
| topic | Databases |
| url | https://arxiv.org/abs/2603.18835 |