AI can translate business questions into draft SQL and explain results. Reliability requires schema context, permission boundaries, controlled execution and domain plausibility checks.
What the concept actually means
A model does not automatically know keys, cardinalities, time semantics or data quality. These must come from the data model, catalogue and validated examples.
Why it matters in the enterprise
Generated SQL first runs read-only with row limits and an explain plan. Data changes, DDL and production access require separate rights and review.
A controlled method
The CTPM practice framework for controllable AI applications uses seven stages: understand the task, clarify context and data, apply AI deliberately, review professionally, handle deviations, approve accountably and document transfer. It is a transparent working framework, not a certification.
- Define task and impact
- Clarify data, context and permissions
- Review against domain criteria
- Control deviations, approval and evidence
CTPM practice example
CTPM practice example: A revenue question is translated into SQL. Analysts review joins, time period, cancellation logic, currency and nulls; control totals are compared with a known report.
Quality and test criteria
The following criteria make quality observable for this use case:
- SQL uses approved objects and least privilege.
- Joins and filters match the business model.
- Control totals and samples are plausible.
- Performance and data volume are bounded before execution.
Risks and common misconceptions
Risks include Cartesian products, wrong join cardinality, dialect errors, sensitive data exposure and syntactically correct SQL with the wrong business meaning.
Example transfer artefact
Transfer artefact: a prompt and review template for SQL with schema excerpt, business definition, safety boundaries, control queries and review.