Free SQL Query Generator

Generate Review-Ready SQL Queries from Plain English

The fastest way to use an AI SQL Query Generator is not to ask for "a query" and hope the tool guesses correctly. The better move is to describe the result you want, choose the right SQL dialect, and give the generator enough schema context to build a query you can actually test.

This tool is built for that workflow. You can generate SELECT statements, reporting queries, joins, aggregations, CTEs, window functions, and safer write-query drafts from plain English, then review the output before running it in your database or BI tool.

What to Enter for Better SQL Output

Start with the business question, then add the database details that remove ambiguity.

The result grain

Tell the generator what each row should represent. For example:

• revenue by month
• active users by day
• orders by customer segment
• latest order per customer
• campaign performance by channel and week

The row grain is what keeps GROUP BY logic, window functions, and deduplication from drifting.

Filters and date boundaries

Be specific about time and inclusion rules:

• last 30 days vs last full calendar month
• include today or exclude partial days
• use order date, paid date, shipped date, or created date
• exclude refunded, canceled, test, or internal records

If your company has a special definition of revenue, active user, churn, or conversion, add that definition in the constraints field.

Schema and join keys

Pasting even a rough schema improves the generated query:

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

Schema details help the SQL generator choose real column names, avoid invented fields, and build JOIN conditions that match your database.

How to Choose the Right SQL Mode

Use SELECT / Read for simple data pulls, dashboard tables, lists, and filtered records. This is the safest default when you need to inspect data.

Use Aggregations when you need counts, sums, averages, conversion rates, cohort totals, or grouped metrics. Include the grouping dimension and metric definition in your prompt.

Use JOIN Builder when the answer depends on two or more tables. Add the relationship if you know it, such as customers.id = orders.customer_id.

Use Advanced when you need CTEs, ranking, deduplication, running totals, or "latest record per user" logic. Mention duplicates, nulls, and tie-break rules.

Use UPDATE, DELETE, or INSERT only when you are ready to review the generated statement carefully. For write queries, ask for the safest possible WHERE clause and run a SELECT with the same predicate first.

Example Prompts You Can Reuse

Reporting query

Show monthly revenue for 2025 by customer segment. Use orders and customers. Exclude refunded orders. Return month, segment, revenue, and order count. Sort by month ascending.

Deduplication query

For each customer, return only their latest order by order_date. If two orders have the same date, keep the one with the higher id. Return customer_id, order_id, order_date, and total_amount.

Safer update query

Generate a PostgreSQL UPDATE statement that changes status from pending to archived for orders before 2024-01-01. Include a precise WHERE clause and a SELECT preview query I can run first.

How to Review the Generated SQL

Do not judge the output only by whether it "looks right." Check the risky parts first:

1. Confirm the SQL dialect matches your database.
2. Read every JOIN condition and make sure it uses the correct keys.
3. Check the WHERE clause before checking the SELECT list.
4. Validate the date range and timezone assumptions.
5. Run a small LIMIT or narrow date window first.
6. Compare row counts against a known report when possible.
7. Review the execution plan if the query will run on large tables.

If you are using the generated SQL inside a larger workflow, Junia's broader AI productivity toolkit can help with the surrounding documentation, reports, and analysis notes after the query is tested.

Common SQL Generator Mistakes to Avoid

• Asking for "all data" instead of naming the columns you need.
• Forgetting to define the row grain before requesting grouped metrics.
• Using vague business terms like revenue or active user without a rule.
• Omitting schema details, then expecting perfect joins.
• Running UPDATE or DELETE statements before previewing affected rows.
• Copying SQL into production without testing it on a small sample.

Final Checklist Before You Run the Query

• The dialect is correct.
• Table and column names exist.
• JOIN keys match the real schema.
• WHERE clauses are narrow enough.
• Date boundaries are explicit.
• Write queries have a preview SELECT.
• The result has been tested on a small range.

Treat the generated SQL as a strong draft, not a magic final answer. The value is speed: you get from business question to reviewable query much faster, then apply the same checks you would use on any SQL you wrote by hand.