Learn the vocabulary of translating a plain-English question into an executable SQL query.
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At standup, a dev mentions letting a business user type a plain-English question and having a language model translate it into an executable SQL query against the company's database. What is this capability called?
Text-to-SQL lets a business user type a plain-English question and has a language model translate that question into an executable SQL query against the company's database, without the user needing to know SQL themselves. A manually written query still requires someone with SQL expertise to phrase the request correctly. This translation capability opens up direct database querying to a much wider set of non-technical users.
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During a design review, the team wants the model translating a question into SQL to be given the target database's actual table and column names so it doesn't guess at a schema it can't see. Which capability supports this?
Schema-aware prompting grounds the model in the target database's real table and column structure, so its translated query references names that genuinely exist rather than a guessed or hallucinated schema. Letting the model guess with no schema information risks a query that fails outright or, worse, silently references the wrong column. This grounding step is what makes a text-to-SQL system reliable against a specific, real database rather than a generic guess.
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In a code review, a dev notices the text-to-SQL system runs a translated query against a read-only replica with a strict row-limit and query timeout before ever showing the user a result. What does this represent?
Sandboxing a generated query's execution with read-only access, a row-limit, and a timeout keeps a malformed or unexpectedly expensive translated query from harming the primary database or running indefinitely. Running every generated query directly against production with full write access and no limits risks real damage from a single bad translation. This sandboxing is a core safety practice for any system that lets an automatically generated query run against real data.
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An incident report shows a translated query correctly matched the user's plain-English question but was silently wrong because the model referenced a deprecated column no longer updated by the application. What practice would prevent this?
Keeping the schema context the model references current and pruned of deprecated or stale columns prevents it from confidently querying a column that looks plausible but no longer reflects real, up-to-date data. Leaving every deprecated column visible with no pruning invites exactly this kind of silently wrong result. This schema-hygiene practice matters because a text-to-SQL query can execute successfully and still return a misleading answer.
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During a PR review, a teammate asks why the team sandboxes every text-to-SQL generated query instead of simply running it directly against the production database like a hand-written query would be. What is the reasoning?
A generated query can be subtly wrong, reference the wrong data, or turn out to be unexpectedly expensive to run, in a way a hand-written query reviewed by an engineer typically wouldn't be. Sandboxing limits how much damage a mistranslation could cause before it reaches a real user or the production database. The tradeoff is the added infrastructure of maintaining a safe, isolated execution environment for every generated query.