write-nql

To turn natural-language questions into validated NQL queries, provide a plain English prompt and the tool drafts NQL against your Narrative datasets. It validates the query using dataset context and data-plane scoping before any execution occurs.

NQL validation checks generated queries against dataset context and the provider's data plane to ensure accuracy. It prevents execution errors by explaining the query in plain English and requiring user confirmation before running it with a materialized-view wrapper.

Yes, you can use natural-language query generation for ad-hoc data exploration on Narrative datasets. It drafts and validates NQL on demand, letting you explore data, qualify datasets, and create repeatable analytic artifacts without manual coding.

Yes, NQL execution requires user confirmation before running. The tool explains the validated query in plain English and only proceeds with execution using a materialized-view wrapper and proper data-plane scoping after you explicitly opt in.

The best way to create repeatable analytic artifacts from plain English prompts is to draft and validate NQL queries against your Narrative datasets. Once validated, the generated NQL serves as a repeatable artifact for ongoing analytical workflows.