Free SQL Query Generator

Generate SQL queries from plain English (without the back and forth)

Writing SQL is not hard. It is just… easy to get wrong when you are moving fast.

You start with a simple question, then it turns into joins, date boundaries, edge cases, and suddenly you are checking whether you should use DATE_TRUNC or DATE_FORMAT, or if SQL Server wants TOP instead of LIMIT. And if you are doing UPDATE or DELETE, one missing filter is a very bad day.

This AI SQL Query Generator is built for that exact moment. You describe what you want in plain English, pick a dialect, optionally paste your schema, and you get a clean query you can run and edit.

What to include in your prompt for better SQL

If you want the output to be surprisingly accurate, include these details in your request. Even if it feels like overkill.

1) The grain of the result

Say what each row should represent.

• revenue by month
• active users by day
• orders by customer_id
• sessions by channel and week

This is the number one thing that prevents weird grouping.

2) Filters and time boundaries

Be explicit about date ranges and inclusions.

• last 30 days vs last full calendar month
• include today or not
• timezone (if it matters)
• exclude refunded or canceled records

3) Output columns you actually want

If you want a tight query, say so.

Instead of “give me everything”, ask for:

• month
• segment
• revenue
• order_count

This naturally avoids SELECT *, and the query becomes easier to review and safer to put into dashboards.

4) Your schema (even a messy version)

Pasting this helps a lot:

orders(id, customer_id, order_date, total_amount, refunded_at, status)
customers(id, customer_segment, country)

Once the tool has column names, it can build join conditions and filters that actually match your database, not some generic guess.

Dialect differences this tool handles (so you do not have to)

A “correct” query in one database can be wrong in another. A few common gotchas:

• PostgreSQL uses DATE_TRUNC, MySQL uses DATE_FORMAT, SQL Server uses DATETRUNC in newer versions or DATEADD patterns.
• LIMIT vs TOP. PostgreSQL, MySQL, SQLite use LIMIT. SQL Server typically uses TOP.
• Identifier quoting is different ("col" vs `col` vs [col]).
• Some functions have different names or argument order across BigQuery, Snowflake, and Oracle.

Selecting the right dialect upfront saves time, and also reduces those subtle syntax errors that waste 20 minutes.

Common query patterns you can generate (with examples)

Use these as copy paste prompts.

Reporting query with join + filters

Prompt idea:

Show total revenue by month in 2025 from orders, exclude refunded, join customers for customer_segment, sort by month.

Aggregation with HAVING

Prompt idea:

Count orders by customer_segment for the last 90 days, only include segments with at least 100 orders.

Deduplication with window functions

Prompt idea:

For each customer, keep only the latest order (by order_date), return customer_id, order_id, order_date, total_amount.

Safe update request

Prompt idea:

Update orders set status = 'archived' where status = 'pending' and order_date < 2024-01-01. Use the safest possible WHERE clause.

You still review it before running, obviously. But it helps you get to a good draft fast.

A quick workflow that keeps you from shipping bad SQL

If you are using this for real work (dashboards, production queries, stakeholder requests), do this every time:

1. Generate the query.
2. Read the WHERE clause first. Then the JOIN conditions.
3. Run with a small limit or date window if possible.
4. Validate row counts against a known baseline.
5. Only then expand to the full range.

And if you want a broader set of writing and productivity tools beyond SQL, you can always explore the full toolkit on Junia AI.

Troubleshooting: why the generated SQL might not match your data

A few common reasons:

• Your column names differ from the tool’s assumptions. Paste schema.
• JOIN key is ambiguous (ex: customer_id exists in multiple tables). Clarify the relationship.
• “Revenue” is defined differently in your business (paid vs shipped vs invoiced). Add a rule in Constraints.
• You have multiple rows per entity and you needed deduplication. Mention duplicates, ask for a window function approach.

If you add a sentence like “There can be duplicate rows per customer per day, keep the latest by updated_at”, the output usually becomes much closer to what you intended.

FAQ style notes (that people usually wonder about)

• If you are doing analytics, always specify the time grain and boundaries.
• If you are doing write queries, always constrain by primary key or a narrow predicate.
• If performance matters, ask for index friendly filtering and explicit columns. Still check the execution plan though.