How to Generate SQL Queries from plain English

Writing complex SQL queries requires knowing the exact syntax for your database dialect, remembering the correct order of clauses, and debugging cryptic error messages. AI SQL generators solve this by letting you describe what you want in plain English and getting production-ready SQL instantly.

What an AI SQL generator can do

• Generate SELECT queries with JOINs, WHERE clauses and aggregations
• Write complex queries with CTEs (Common Table Expressions)
• Generate INSERT, UPDATE and DELETE statements
• Create stored procedures and functions
• Optimise existing queries for performance
• Translate between SQL dialects (MySQL → PostgreSQL)

Examples: English to SQL

Example 1: Simple aggregation

Plain English: "Get the total revenue by product category for the last 30 days, sorted by highest revenue first"

SELECT
    p.category,
    SUM(oi.quantity * oi.unit_price) AS total_revenue,
    COUNT(DISTINCT o.id) AS order_count
FROM orders o
JOIN order_items oi ON o.id = oi.order_id
JOIN products p ON oi.product_id = p.id
WHERE o.created_at >= NOW() - INTERVAL '30 days'
GROUP BY p.category
ORDER BY total_revenue DESC;

Example 2: Finding duplicates

Plain English: "Find all customers who have more than one account with the same email address"

SELECT
    email,
    COUNT(*) AS account_count,
    array_agg(id) AS account_ids
FROM customers
GROUP BY email
HAVING COUNT(*) > 1
ORDER BY account_count DESC;

Example 3: Running totals with window functions

Plain English: "Show monthly sales with a running total for the current year"

WITH monthly_sales AS (
    SELECT
        DATE_TRUNC('month', created_at) AS month,
        SUM(amount) AS monthly_revenue
    FROM orders
    WHERE EXTRACT(YEAR FROM created_at) = EXTRACT(YEAR FROM NOW())
    GROUP BY 1
)
SELECT
    month,
    monthly_revenue,
    SUM(monthly_revenue) OVER (
        ORDER BY month
        ROWS UNBOUNDED PRECEDING
    ) AS running_total
FROM monthly_sales
ORDER BY month;

SQL dialect differences to watch for

Different databases use different syntax for the same operations. Common differences:

• Date arithmetic — NOW() - INTERVAL '30 days' (PostgreSQL) vs DATE_SUB(NOW(), INTERVAL 30 DAY) (MySQL)
• String concatenation — || (PostgreSQL, SQLite) vs CONCAT() (MySQL)
• Limiting rows — LIMIT 10 (PostgreSQL, MySQL) vs TOP 10 (T-SQL) vs FETCH FIRST 10 ROWS ONLY (BigQuery)
• Boolean values — TRUE/FALSE (PostgreSQL) vs 1/0 (MySQL, SQLite)

When to use an AI SQL generator vs writing manually

AI SQL generators are best for:

• Queries you haven't written before — unfamiliar syntax
• Complex analytical queries with multiple JOINs and window functions
• Quick prototypes — get a working query fast, refine later
• Cross-dialect translation — you know MySQL but need BigQuery

Write manually when:

• Performance is critical — hand-tune indexes and query plans
• The query is simple — SELECT * FROM users WHERE id = 1 is faster to type
• You need precise control over execution order

The best SQL generator is one that understands your schema. Always paste your table definitions for more accurate, immediately usable results.