AI SQL Query Generator

How to generate SQL from your own database schema

A useful SQL generator needs the structure of your data and the meaning behind it. “Customers without orders” might mean no orders ever, no paid orders this month, or no completed deliveries. Each requires a different query. A table name cannot resolve that decision for you.

1. Connect an approved data source. Choose the actual engine and follow the connection instructions. For analysis, use a dedicated read-only database role restricted to the necessary tables or reporting views. Check which schema is available before asking questions.
2. Add business context. Explain join keys, status codes, reporting dates, currencies and metric definitions. Use documentation and SQL training examples for recurring rules. Do not put passwords or unrelated personal information into a prompt.
3. Describe a bounded result. Specify columns, one-row-per-what, filters, order and tie-breaks. Ask for clarification when a definition is missing. A written request to avoid execution is not a permission boundary; enforce access through the database role.
4. Inspect and reconcile. Check the SQL and compare returned rows with a small known answer. Use follow-up questions to change the analysis, and recheck the result after each change. Keep any write-query evaluation in a separate, explicitly approved test environment.

AskYourDatabase supports conversational follow-ups and result visualization. If your goal is a repeatable dashboard, the SQL dashboard worked guide covers metric definitions and checks across widgets. Generating one query is only the first part of that workflow.

A SQL-generation prompt you can reuse

Fill in this brief using the real schema. For a first question, choose one business measure and a short, fixed time window. Avoid “recent,” “best” or “active” unless you explain exactly what those words mean in your database.

Database engine and version: [your engine/version]
Allowed tables/views: [schema-qualified names]
Relationships: [join keys and one-to-many relationships]
Question: [the business decision and measure]
Output grain: [one row per customer, order, month, etc.]
Filters: [status, date range, time zone and currency]
Missing data: [NULL and zero-row behavior]
Output: [columns, ordering, tie-break and row limit]
Before generating SQL: ask about missing business definitions.
Explain the joins and assumptions; do not invent missing columns.

For example, request: “List every customer with no paid order from September 1 through September 30, 2026. Exclude cancelled orders from the purchase test. Include customers who never ordered. Return customer ID and name, ordered by customer ID.” The output grain is one customer; the date filter belongs to the order test.

Worked example: customers without a paid order

This complete read-only SQL block creates temporary query results through CTEs; it does not create or change stored tables. All names and orders are fictional. We executed the reference in local SQLite and PGlite 0.5.8 (PostgreSQL 18.3). No AI product was benchmarked, and execution on other database engines is not claimed.

WITH customers AS (
  SELECT 1 AS customer_id, 'Ada' AS name
  UNION ALL SELECT 2, 'Bea'
  UNION ALL SELECT 3, 'Chen'
  UNION ALL SELECT 4, 'Dev'
), orders AS (
  SELECT 101 AS order_id, 1 AS customer_id,
         'paid' AS status, '2026-09-02' AS ordered_on
  UNION ALL SELECT 102, 2, 'cancelled', '2026-09-03'
  UNION ALL SELECT 103, NULL, 'paid', '2026-09-05'
  UNION ALL SELECT 104, 3, 'paid', '2026-08-31'
  UNION ALL SELECT 105, 1, 'paid', '2026-09-10'
  UNION ALL SELECT 106, 4, 'paid', '2026-10-01'
)
SELECT c.customer_id, c.name
FROM customers c
WHERE NOT EXISTS (
  SELECT 1
  FROM orders o
  WHERE o.customer_id = c.customer_id
    AND o.status = 'paid'
    AND o.ordered_on >= '2026-09-01'
    AND o.ordered_on < '2026-10-01'
)
ORDER BY c.customer_id;

Expected result: customer 2 (Bea), customer 3 (Chen), customer 4 (Dev). Ada has two paid September orders. Bea has only a cancelled order, Chen has a paid order before September, and Dev has one exactly at the excluded October boundary. The unassigned order with a NULL customer ID matches nobody.

NOT EXISTS asks whether a matching row is absent. Replacing it with customer_id NOT IN (SELECT customer_id ...) is dangerous here: the paid September subquery includes NULL, so customers without matches do not pass that predicate. In this fixture, that incorrect version returns no customers. A query can run without an error and still lose every expected row.

The dates here are deliberately fixed ISO-formatted strings. In your database, use the actual date or timestamp type, correct parameter binding, and the agreed reporting time zone. A half-open interval includes the start and excludes the next period’s start; do not append an assumed “last second of the month.”

Follow-up: include paid-order counts for every customer

Keep the same two CTEs and replace the final SELECT with the following. The paid/date filters stay in the join condition so customers without a match remain in the result. Count the matched order ID, not COUNT(*), which also counts the preserved customer row.

SELECT c.customer_id, c.name, COUNT(o.order_id) AS paid_orders
FROM customers c
LEFT JOIN orders o
  ON o.customer_id = c.customer_id
 AND o.status = 'paid'
 AND o.ordered_on >= '2026-09-01'
 AND o.ordered_on < '2026-10-01'
GROUP BY c.customer_id, c.name
ORDER BY c.customer_id;

Expected counts by customer ID: 1 → 2, 2 → 0, 3 → 0, 4 → 0. Multiple orders do not create multiple customer rows because the result groups at customer grain. If you add order items or refunds, check whether those one-to-many joins multiply orders before counting or summing.

Make one change and predict the answer

Change Bea’s cancelled order to paid: the first query should now return only Chen and Dev, and Bea’s paid-order count should become one. Change Dev’s order date to September 30: Dev should leave the first result. Keep the expected answer independent of the generated SQL rather than asking the same model to certify itself.

These checks illustrate query review. For a reusable evaluation set and a scorecard, use our text-to-SQL acceptance kit. Broader definitions and system-design tradeoffs belong in the natural-language querying guide.

Primary syntax references: PostgreSQL subquery and NULL behavior, MySQL EXISTS / NOT EXISTS, and SQL Server EXISTS. Check documentation for the version you run; similar operators do not make all SQL syntax interchangeable.

Choose the SQL workflow that fits the task

AskYourDatabase is a connected assistant, not a promise of an anonymous free browser tool or a drag-and-drop query designer. Check current plan terms rather than assuming that downloading the app includes unlimited usage. For private hosting requirements, review on-premises deployment boundaries before choosing a plan.

Use the correct database dialect

Select the engine and version before generating SQL. Pagination, JSON operators, identifier quoting and date functions vary. SQL Server’s TOP is not MySQL’s LIMIT; PostgreSQL JSONB expressions need different syntax elsewhere. Supported non-SQL databases also use different query languages.

• PostgreSQL query examples cover latest-row selection, conditional totals and JSONB.
• MySQL query generation and the MySQL connection guide cover that database workflow.
• SQL Server query generation is the starting point for T-SQL requests.

Check correctness, access and cost separately

For correctness, verify schema names, row grain, joins, NULLs, dates and expected totals. For access, use database permissions or scoped reporting views; prompt wording is not authorization. For performance, inspect execution plans and representative data with the database owner. A syntactically valid query is not automatically optimized, and LIMIT does not guarantee a cheap scan or aggregation.

Desktop database connectivity and model processing are separate paths. Depending on deployment and model configuration, schema, questions and relevant query results may be sent for inference. Hosted services can also retain conversation or result history. Read the security and data-flow documentation before connecting sensitive data. Do not infer offline processing from the presence of a local app.

Once a query passes known-answer checks, save the approved business definition and use it consistently. If results change unexpectedly after a schema or model update, rerun those checks before relying on the new answers. Measure useful completed analyses and actual product adoption separately from generating SQL text.

Frequently asked questions