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Any text-to-SQL benchmark should address difficulties of real-world data stores
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Overview
Text-to-SQL benchmarks must consider the complexities and challenges posed by real-world data stores. Effective evaluation should include factors like data variability, schema complexity, and query performance.
Summary from cacm.acm.org.
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Hacker News · cacm.acm.org
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Based on 5 sampled comments from this thread. Analyzed 2026-07-23.
- Praise: SQL generation is quite good, 90% of the problem is data quality — if you clean up your data, LLMs can shine in generating queries.
- Praise: Text to SQL can absolutely work if you design the system correctly — with the right architecture, it’s not just a pipe dream.
- Praise: Involving a human operator in the query generation loop can significantly improve results — it’s all about collaboration between LLMs and users.
- Concern: Business users treating LLM-generated SQL as gospel is a recipe for disaster — without validation, critical errors could go unchecked.
- Concern: An LLM can't read the user's mind to eliminate ambiguity — until that changes, misinterpretations will persist.
- Concern: Single-shot approaches to SQL generation are a dead end — without iterative hypothesis generation, you're just guessing.