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Intermediate Professionals

Mastering Natural-Language Queries on Your Data

Learn how OUPI turns plain-language questions into reliable, code-verified answers on your own datasets. This intermediate course covers the query engine's six-step pipeline, why code-based arithmetic beats model guessing, how to craft effective questions, the security safeguards protecting your data, visualizations and saved queries, and the limits you should know. Hands-on practice included.

7 lessons· 15 min· Updated on 15 September 2026

Course outline

1 Welcome to OUPI Data: Your AI-Native Data Hub Understand what OUPI Data is, how to import a dataset, and how it serves as the foundation for natural-language querying. 2 From Question to Answer: The Six-Step Pipeline Trace the full journey of a natural-language query — from your question through code generation, sandboxed execution, summary writing, number verification, chart suggestion and follow-up proposals. 3 Why Code Beats Guessing: The Reliability Argument Explain why having arithmetic performed by generated code on real rows — with a verification step comparing the summary to computed data — is more reliable than letting a language model approximate. 4 Asking Great Questions Learn practical techniques for writing effective queries: naming columns precisely, specifying periods and groupings, asking one thing at a time, and handling ambiguous column names. 5 Security and Data Privacy Understand the security guards that inspect generated code before execution, the sandboxed environment, and the guarantee that your data never leaves the platform during processing. 6 Visualizations, Saved Queries and Team Sharing Use charts generated from query results, pin them to your dashboard, save queries for re-use on refreshed data, and share them with your team. 7 Knowing the Limits and Going Further Recognise what the engine cannot do — missing columns, structure sampling on very large datasets, complex modelling — and know when to escalate to a mission or use Intelligence alerts.

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