# How to Use AI to Write SQL Queries from Plain English

> Source: <https://dev.to/leveragenotes/how-to-use-ai-to-write-sql-queries-from-plain-english-4fal>
> Published: 2026-07-08 09:00:31+00:00

If you've ever stared at a complex schema and spent 20 minutes writing a query you could describe in one sentence, this walkthrough is for you. I'll show you exactly how to prompt an AI assistant to generate accurate, production-ready SQL from plain English — including the context you need to provide, the prompts that work, and how to verify what comes back.

The usual failure mode: you paste a vague question into ChatGPT or Claude, get back a query that looks plausible, run it, and get wrong results or an error. The fix isn't a better AI — it's a better prompt. AI models write SQL accurately when you give them the same context a senior teammate would need.

Never ask for a query without first providing your table definitions. Copy the `CREATE TABLE`

statements directly from your database.

```
-- Paste this into your prompt first
CREATE TABLE orders (
  id SERIAL PRIMARY KEY,
  customer_id INT NOT NULL,
  status VARCHAR(20) NOT NULL,  -- 'pending', 'shipped', 'cancelled'
  total_amount NUMERIC(10, 2),
  created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE customers (
  id SERIAL PRIMARY KEY,
  email VARCHAR(255) UNIQUE NOT NULL,
  signup_date DATE NOT NULL,
  plan VARCHAR(20) DEFAULT 'free'  -- 'free', 'pro', 'enterprise'
);
```

If your schema is large, include only the tables relevant to your question.

Use this template every time:

```
You are a SQL expert working with PostgreSQL.

Here are my table definitions:
[PASTE SCHEMA HERE]

Write a query that: [PLAIN ENGLISH DESCRIPTION]

Requirements:
- Use CTEs if the logic has more than one step
- Add a comment explaining any non-obvious join condition
- Do not use SELECT * — name every column
```

**Example request:**

```
Write a query that returns, for each pro or enterprise customer who signed up
in the last 90 days, their email, total number of orders, and total spend —
but only where total spend exceeds $500. Sort by total spend descending.
```

**What the AI returns:**

```
-- Customers on paid plans, signed up in the last 90 days
WITH recent_paid_customers AS (
  SELECT id, email
  FROM customers
  WHERE plan IN ('pro', 'enterprise')
    AND signup_date >= CURRENT_DATE - INTERVAL '90 days'
),
customer_order_totals AS (
  SELECT
    c.email,
    COUNT(o.id)          AS order_count,
    SUM(o.total_amount)  AS total_spend
  FROM recent_paid_customers c
  -- Left join so customers with zero orders still appear; filter below
  JOIN orders o ON o.customer_id = c.id
  WHERE o.status != 'cancelled'
  GROUP BY c.email
)
SELECT
  email,
  order_count,
  total_spend
FROM customer_order_totals
WHERE total_spend > 500
ORDER BY total_spend DESC;
```

Clean, readable, and correct on the first try — because the prompt had enough context.

Ask the AI to explain the query back to you in plain English:

```
Explain this query step by step. Call out any assumptions you made
about the data or business logic.
```

This surfaces hidden assumptions fast. If the explanation doesn't match your intent, correct it in the next message — the model retains the schema context in the same conversation.

Follow up with edge-case questions in the same thread:

```
What happens if a customer has no orders at all? Will they appear in results?
Rewrite using a LEFT JOIN if needed so they show up with zero values.
```

The model will revise the query and explain the change. This loop — generate, explain, stress-test, revise — usually gets you to a production-safe query in three to four messages.

`CREATE TABLE`

statements`EXPLAIN ANALYZE`

on the result for any query touching large tablesThat's the full loop. No magic — just structured context.

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