SQL Query Generator

Type your requirement in plain English, for example "find all orders placed in the last 7 days where the total exceeds 200 and the customer has not yet been contacted." The AI parses that description, identifies the tables and conditions implied, writes a properly formatted SQL query, and then explains each part so you can verify it against your actual schema before running it.

The generated query is written in standard SQL and annotated with compatibility notes for MySQL, PostgreSQL, SQL Server, and SQLite. Where syntax differs, for example window functions, date arithmetic, or string concatenation, the response points out the specific variation for each database so you can adapt the query without trial and error.

You can describe what you want without knowing exact names and the AI will use conventional placeholder names like "orders," "customers," and "product_id" that you then rename to match your schema. If you do include your real table and column names in the prompt, the query will use them directly and the output requires no renaming at all.

Yes. Describe a multi-table relationship in plain English and the AI will choose the appropriate JOIN type, whether INNER, LEFT, or otherwise, based on what you need. For aggregations over time ranges, running totals, or rankings, it will use window functions where they fit and explain why that approach is better than a correlated subquery in that context.

The output is plain text inside a code block, so you copy it into your SQL editor and change whatever you need. Because each clause is explained individually, you know exactly which line to edit rather than guessing at the structure.

Most queries, including the explanation and optimization notes, are returned within a few seconds. More complex requests involving multiple JOINs or CTEs may take slightly longer, but the response streams in so you start reading the query immediately rather than waiting for the whole response to complete.

Yes. The queries are yours to use in any project. As with any generated code, review the logic against your actual schema and test on representative data before running it on production. The explanation and optimization notes in the output are there specifically to help you do that review quickly.

A general chatbot requires you to specify the database dialect, ask for an explanation, request optimization tips, and handle follow-up in separate messages. This tool is preconfigured to always return the query, the explanation, performance notes, and cross-database compatibility in one structured response. You get the same depth every time without writing a long prompt.

Your input is processed to generate the query and is not shared with third parties. If your description contains sensitive schema details, consider using generic table and column names in the prompt and renaming them in the output afterward.

Be specific about the data you want, the conditions that filter it, how it should be sorted or grouped, and which database you are targeting. For example: "Get monthly revenue per customer from the orders table where status is completed, grouped by customer_id and month, for PostgreSQL" will produce a tighter result than "show me sales by customer." The more constraints you name up front, the less editing the output needs.