Technical
Skip the syntax lookup. Describe what you need from your database and get a correct, explained SQL query ready to run.
AI-generated output
Describe your data requirement above and click Generate to get your SQL query.
Describe what you want to retrieve or modify in plain English. Be specific about what columns you need, what conditions to apply, and how the results should be sorted or grouped.
Enter your table names and the relevant columns. The more accurately you describe your schema, the more accurate the generated query will be.
You get a working SQL query formatted for your chosen database, a plain-English explanation of each clause, the expected output shape, and any potential performance considerations.
For example, describe "find all customers who made more than 3 orders in the last 30 days, ordered by total spend descending" with tables "customers" and "orders", and you get a query using a JOIN, GROUP BY, HAVING, and ORDER BY — with an explanation of why HAVING is used instead of WHERE for the aggregation filter.
The performance note might flag that this query will benefit from an index on orders.customer_id and orders.created_at — a consideration that is easy to miss when writing queries manually.
For standard SELECT, INSERT, UPDATE, and DELETE operations on clearly described schemas, accuracy is high. The more precise your table names and column names, the more accurate the output. Always review and test generated queries against a development or staging database before running against production — especially for UPDATE and DELETE operations.
SQL syntax varies between databases in important ways. Date functions differ significantly (GETDATE() in SQL Server, NOW() in MySQL, CURRENT_TIMESTAMP in PostgreSQL). String concatenation uses different operators. Window function support varies. Selecting the correct database ensures the generated query will actually run in your environment.
Yes — describe the relationship between tables in plain English (e.g. "orders belong to customers via orders.customer_id = customers.id") and the AI will build the appropriate JOIN structure. For highly complex queries with many CTEs, subqueries, or recursive elements, providing the schema clearly and being explicit about what each step should do produces the best results.
Treat AI-generated SQL as you would code from any source: review it before running it. For SELECT queries, the risk is low — at worst you get wrong results. For UPDATE, INSERT, and DELETE queries, always test on a staging database first, consider wrapping in a transaction, and ensure you understand what the WHERE clause will match before executing.
Generate regular expressions from plain English descriptions with full explanations.
Analyse your text for readability, complexity, and audience fit with specific improvement suggestions.
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