Text-to-SQL assistants: make the question precise before running the query
Design database question answering around metric definitions, restricted execution, result validation, and clear limits on what an answer establishes.
A generated SQL statement can execute successfully and still answer the wrong question. It might use the wrong date column, count a customer twice, or confuse an order with a completed sale. Syntax is only one layer of correctness in a database assistant.
A useful text-to-SQL workflow establishes the business meaning of a question, controls what the query may access, and checks the interpretation of the returned data. The language model proposes a translation; the application remains responsible for the execution boundary.
Resolve the metric before the query
Consider a hypothetical equipment-rental business. A manager asks which branch grew fastest last month. Growth could mean bookings, fulfilled rentals, revenue, or new customers. Last month could use the branch's local calendar or a central reporting timezone.
Provide a small governed metric dictionary with definitions, permitted dimensions, and relevant date fields. If the question remains ambiguous, ask a targeted clarification. A fluent answer based on an arbitrary definition is less useful than a brief request for the missing decision.
Keep the dictionary version with the query record. When finance changes a definition, older reports should remain explainable. The assistant should not silently reinterpret historical answers using today's rules.
Expose a narrow analytical surface
Give the assistant only the schema and data access needed for the task. Curated views can hide irrelevant tables and simplify joins. The execution identity should have narrowly scoped privileges; a prompt saying read only is not a database permission.
PostgreSQL documents read-only transaction behavior and statement timeouts, which can form part of an execution policy. They are layers within a broader design, not a complete sandbox. Functions, roles, accessible data, and resource limits still require review.
For the rental example, prefer a controlled reporting environment over unrestricted production access. Apply authorization using the signed-in user's verified scope. A branch manager's question should not grant access to another branch merely because the model generated a query that names it.
Validate before and after execution
Use a parser and an explicit allowed-query policy where arbitrary SQL is permitted. Restrict statement types, referenced objects, result volume, and execution time. Do not rely on a text search for forbidden words: SQL syntax and execution behavior are more complex than a keyword list.
For common questions, a structured query plan compiled by ordinary code can provide a smaller and more inspectable surface than free-form SQL. The model selects from permitted metrics and filters; the server builds the statement with validated identifiers and bound values.
After execution, check column types, units, empty results, and suspicious duplication. A successful database response is evidence of execution, not evidence that the business question was answered correctly.
Test joins with deliberately awkward data
Build a small fixture where the expected result can be calculated by hand. Include one rental with several line items, a cancellation, a late return, a branch with no transactions, and a customer who books twice.
Ask the growth question against this fixture. If a join multiplies rows, the inflated total becomes visible. Add boundary dates near midnight and month end to test the chosen timezone and period definition.
Keep these checks independent of the generated explanation. A model can confidently explain a wrong query. Review the actual result and the query plan rather than accepting the narrative as proof.
Communicate what the data can support
Show the metric definition, date range, relevant filters, and important exclusions beside the answer. If the prior period is zero, percentage growth may be undefined or require a stated business convention. Do not invent a percentage just to complete a chart.
When a result is truncated, make that visible before summarizing it as a complete ranking. If the query times out, report incomplete analysis rather than presenting a partial set as definitive. The next action might be narrowing the period or choosing a pre-aggregated view.
Let authorized users inspect the underlying query or a plain-language plan. Different audiences need different detail, but both should be able to understand the assumptions that materially affect the answer.
Release with a recoverable workflow
Start with reviewed analytical questions and a limited data surface. Record validation failures and recurring ambiguities so the metric dictionary can improve. Measure accepted answers and review effort rather than SQL execution rate alone.
An effective database assistant reduces the distance between a question and a trustworthy calculation. It does so by making interpretation explicit and keeping data access under application control, not by treating executable SQL as the final definition of success.