AI SQL Generator (Text to SQL)
Describe the query you need — get SQL for your chosen dialect.
The AI SQL Generator converts a plain-English request into a ready-to-run SQL query for the dialect you choose — PostgreSQL, MySQL, SQLite or SQL Server. It handles joins, aggregates, grouping, filters and date ranges, inferring sensible table and column names when you don't provide a schema.
- ✓Natural language → SQL in one click
- ✓PostgreSQL, MySQL, SQLite and SQL Server
- ✓Joins, GROUP BY, aggregates, window functions and date filters
- ✓Paste your schema for more accurate column names
How to Turn Text into SQL
Describe what you want — for example "monthly revenue for 2024 grouped by product category" — pick a dialect, and click Generate. For the most accurate results, include your table and column names (or paste a CREATE TABLE statement) so the query references your real schema. To turn existing data into tables, try JSON to SQL or CSV to SQL.
What You Can Build with the AI SQL Generator
- ▸Ad-hoc analytics — Turn a business question — "revenue by month for 2024" — into a runnable query without hand-writing joins or GROUP BY.
- ▸Dashboards & reports — Draft the SQL behind a KPI, chart, or scheduled report in seconds, then refine it.
- ▸Learning SQL — See exactly how a plain-English request maps to real syntax, and compare the same query across PostgreSQL, MySQL, SQLite and SQL Server.
- ▸Data cleanup & migration — Generate UPDATE, DELETE and transformation queries for one-off fixes and schema migrations.
- ▸Prototyping a new schema — Get working queries for tables you just designed, before wiring up an ORM or building endpoints.
- ▸Interview & practice prep — Check your own solution against an AI-written query for classic problems like "second highest salary per department".
Tips for Accurate Text-to-SQL
The more context you give, the better the SQL. Include your real table and column names (or paste a CREATE TABLE statement), name the dialectyou use, and be specific about filters, sorting and limits. For a request like "top 10 customers by total spend in the last 30 days," mention the orders and customers tables and the date column so the AI joins them correctly. Always review generated SQL and run it against a safe dataset before production — AI is a fast first draft, not a substitute for testing. To turn existing data into tables, use JSON to SQL or CSV to SQL, and tidy the output with the SQL Formatter.
SQL Dialect Differences to Know
The same logical query is often written differently across engines. The generator handles this for you, but knowing the differences helps you sanity-check the output:
| Feature | PostgreSQL | MySQL | SQL Server |
|---|---|---|---|
| Limit rows | LIMIT 10 | LIMIT 10 | TOP 10 (in SELECT) |
| Auto-increment PK | GENERATED ALWAYS AS IDENTITY | AUTO_INCREMENT | IDENTITY(1,1) |
| String concatenation | a || b | CONCAT(a, b) | a + b |
| Current timestamp | NOW() | NOW() | GETDATE() |
| Upsert | INSERT ... ON CONFLICT DO UPDATE | INSERT ... ON DUPLICATE KEY UPDATE | MERGE |