'安全查询空间'让AI修复SQL错误而无需重来

September 4, 2026 'Safe query space' lets AI fix SQL errors without starting over by The Korea Advanced Institute of Science and Technology (KAIST) edited by Sadie Harley, reviewed by Robert Egan Sadie Harley Scientific Editor Meet our editorial team Behind our editorial process Robert Egan Senior Editor Meet our editorial team Behind our editorial process Editors' notes This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: fact-checked trusted source proofread The GIST Add as preferred source Comparison of the SQL error correction processes used by conventional methods and SafeQL. Credit: KAIST "Find the best-selling product from last year." When an AI system attempts to answer a question like this by querying a company database, even a single reference to a nonexistent item can cause the query to fail. Until now, correcting such an error often required regenerating the entire SQL query from scratch. A research team led by Professor Min-Soo Kim from the School of Computing has developed SafeQL, a technology that instead identifies and fixes only the erroneous part. The technology is expected to make AI-powered data retrieval faster and more accurate, accelerating the adoption of AI work assistants in enterprise environments. The findings were presented at the International Conference on Very Large Data Bases (VLDB), held in Boston, U.S., from Sept. 1–5. Text-to-SQL technology enables AI systems to convert everyday questions, such as "Which product saw the largest increase in sales last year?" or "Which items are running low in stock?" into SQL queries. This allows users to retrieve sales, customer and inventory data using natural language without having to understand complex database commands. However, AI systems can make mistakes when generating SQL. For example, they may refer to a table or column that does not exist or join tables incorrectly. These errors can prevent the query from running and leave the AI unable to retrieve the requested data. SafeQL's efficient SQL search process using pruning. Credit: KAIST Rewriting one word at a time Conventional correction methods send the database error back to a large language model (LLM) and ask it to regenerate the entire query. This is similar to rewriting an entire report to correct a single word. In the process, parts that were already correct may be altered, new errors may be introduced, and repeated calls to the LLM increase both cost and processing time. SafeQL takes a different approach. Instead of discarding and regenerating the entire query after an execution failure, it interprets feedback from the database management system to precisely locate the faulty component, such as a relation, attribute, function or value. It then incrementally repairs that component while preserving the valid structure and logic of the original query. To achieve this, the research team developed a "safe query space" approach. Among the candidate corrections that can be executed on the database, SafeQL searches for the one closest to the query originally generated by the AI. The system prioritizes the most promising candidates and filters out unsuitable ones in advance, reducing the time required for correction. The team implemented SafeQL as a PostgreSQL extension and integrated it with the database system's parser, binder and type analyzer. This enables SafeQL to precisely locate errors even in complex SQL queries. The system calls the LLM again when search-based refinement cannot resolve the error within a predefined threshold, thereby reducing unnecessary AI use. Benchmark gains and enterprise use The research team evaluated SafeQL using BIRD and Spider, two widely used benchmarks for assessing the database querying capabilities of AI systems. On the BIRD benchmark, SafeQL resolved execution errors in up to 87.4% of initially erroneous SQL queries and improved execution accuracy by up to 5.8 percentage points over the unrefined baseline. Compared with regenerating the entire query, SafeQL reduced token use by a factor of up to 15.1 and refinement latency by a factor of up to 29.6. SafeQL is expected to be particularly useful for enterprises that handle large volumes of data requests. If an error occurs while an AI system is searching internal sales, customer or inventory data, SafeQL can repair only the affected part instead of regenerating the entire query. This can reduce the cost and time required to operate enterprise AI systems and support reliable work automation powered by AI agents and corporate data. Professor Kim said, "For AI to perform real-world tasks in enterprise environments, it must be able to accurately retrieve the data it needs." He added, "When AI makes an error during a database search, SafeQL fixes only the affected part instead of starting over from scratch. By reducing errors, costs and processing time, we expect the technology to accelerate reliable AI-powered work automation." Geonho Lee, a Ph.D. student in the KAIST School of Computing, participated in the study as first author, with Professor Kim serving as corresponding author. More information Geonho Lee et al, SafeQL: Search-based Refinement for Safe and Efficient LLM-based Text-to-SQL, Proceedings of the VLDB Endowment (2026). DOI: 10.14778/3819518.3819545 SafeQL: safeql.dev/ Provided by The Korea Advanced Institute of Science and Technology (KAIST) Who's behind this story? Sadie Harley BSc Life Sciences & Ecology. Microbiology lab background with pharmaceutical news experience in oil, gas, and renewable industries. Full profile → Robert Egan Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile → Citation: 'Safe query space' lets AI fix SQL errors without starting over (2026, September 4) retrieved 8 September 2026 from https://techxplore.com/news/2026-09-safe-query-space-ai-sql.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only. 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