Module
Module 6 of 6Lesson 2 of 3~14 min

Have an AI write SQL, and check it

An AI can draft a SQL query in seconds that runs fine yet doesn't necessarily measure what you are after. You learn to give it the right context, have it explain its choices and review its work before trusting the result. A real time saver, without decisions based on a wrong number or data leaks.

Take this lesson: your first course is freeSee the full syllabus

Lesson objective

By the end of this lesson, you will be able to give an AI the schema context it needs (tables, columns, values, business rules, dialect), have it draft and explain a query, then review the query with a ten-point checklist before using its result.

Topics covered

  • SQL with AI
  • text-to-SQL
  • SQL prompt
  • query review
  • schema context

Where it fits

Query safely, with AI

How do I access real data without risk to production or to privacy, and how do I have an AI write SQL without getting it wrong?

Lessons in this module

  1. Access real data without risk, for the product and for people
  2. Have an AI write SQL, and check it (this lesson)
  3. Final workshop: your feature's data model and your ten queries

What you will learn in the course

This lesson is part of the course Model your product’s data and query it with SQL

  • Read a data model (tables, primary and foreign keys, cardinalities, entity-relationship diagram) and connect it to the product's screens and rules.
  • Model the data of a feature (entities, attributes, relationships, keys, cardinalities) and get the model validated by the engineering team.
  • Compare a relational database and a document (NoSQL) database for a product need and justify the choice based on expected queries, consistency and change.
  • Write queries that filter, sort and aggregate (SELECT, WHERE, ORDER BY, GROUP BY, HAVING) while handling NULL values and dates correctly.
  • Combine several tables with INNER and LEFT joins, and check that the result neither loses nor duplicates any row.
  • Turn a product question into a verifiable query, using CTEs and simple window functions (ROW_NUMBER, LAG, SUM OVER).
  • Query data read-only while protecting personal data, and have an AI draft SQL while checking every query before using its result.

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