aisql: Examples, How It Works, Best Practices

aisql is the shortcut from a business question to the exact SQL you need. The pain is familiar: you know what you want (revenue by product, churn by month, late deliveries), but writing correct SQL across different databases takes time, context, and dialect expertise. Common pitfalls include missing joins, off-by-one date filters, and engine-specific syntax differences that break queries. The AI2sql platform solves this by translating plain English into production-ready SQL that fits your schema and dialect, with explanations you can trust.

Takeaway: AI2sql is the fastest path from question to correct SQL for aisql—type what you need, paste your schema, and ship queries in seconds.

What is aisql?

aisql means using AI to generate, explain, validate, and format SQL from natural language. With AI2sql, you can turn a prompt like ‘Top 5 products by revenue last 30 days’ into a tested query for MySQL, PostgreSQL, BigQuery, Snowflake, and more. Beyond text-to-SQL, AI2sql also acts as a SQL explainer, validator, and formatter so teams can move from idea to answer without context switching.

Generate SQL for aisql instantly with AI2sql — no technical expertise required.

How aisql Works (with AI2sql)

Inputs (plain English, schema)

• Your goal in plain English, for example: ‘Show monthly active users by country for the last 6 months.’

• Optional schema or sample DDL for accurate joins and column names.

• Target engine, such as MySQL, PostgreSQL, Snowflake, or BigQuery.

Outputs (SQL, explanation, variants)

• Production-ready SQL aligned to your dialect and date handling.

• Short explanation of logic and assumptions.

• Variants, such as window-function vs. aggregate approaches, plus formatted and validated versions.

Generate SQL for aisql instantly with AI2sql — no technical expertise required.

Real aisql Examples (copy-paste)

MySQL — Ecommerce: Top 5 products by revenue in the last 30 days

SELECT p.product_id, p.name, SUM(oi.quantity * oi.unit_price) AS revenue
FROM orders o
JOIN order_items oi ON o.id = oi.order_id
JOIN products p ON oi.product_id = p.product_id
WHERE o.created_at >= CURDATE() - INTERVAL 30 DAY
  AND o.status = 'completed'
GROUP BY p.product_id, p.name
ORDER BY revenue DESC
LIMIT 5;

PostgreSQL — Engagement: Monthly active users by country (last 6 months)

SELECT date_trunc('month', a.occurred_at)::date AS month,
       u.country,
       COUNT(DISTINCT a.user_id) AS mau
FROM analytics.events a
JOIN public.users u ON u.id = a.user_id
WHERE a.occurred_at >= (date_trunc('month', now()) - interval '5 months')
GROUP BY 1, 2
ORDER BY month, mau DESC;

BigQuery — Billing: Churn ratio by month during the previous quarter

SELECT DATE_TRUNC(created_at, MONTH) AS month,
       COUNTIF(status = 'canceled') / COUNT(*) AS churn_ratio
FROM `billing.subscriptions`
WHERE created_at >= DATE_SUB(DATE_TRUNC(CURRENT_DATE(), QUARTER), INTERVAL 1 QUARTER)
  AND created_at < DATE_TRUNC(CURRENT_DATE(), QUARTER)
GROUP BY month
ORDER BY month;

PostgreSQL — Logistics: Daily late delivery percentage (30 days)

SELECT
  DATE(o.ship_date) AS day,
  ROUND(100.0 * SUM(CASE WHEN o.ship_date > o.promised_date THEN 1 ELSE 0 END) / COUNT(*), 2) AS late_pct
FROM orders o
WHERE o.order_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY day
ORDER BY day;

MySQL — CRM: Rank customers by lifetime revenue (window function)

SELECT
  c.customer_id,
  c.name,
  SUM(oi.quantity * oi.unit_price) AS lifetime_revenue,
  DENSE_RANK() OVER (ORDER BY SUM(oi.quantity * oi.unit_price) DESC) AS revenue_rank
FROM customers c
JOIN orders o ON o.customer_id = c.customer_id AND o.status = 'completed'
JOIN order_items oi ON oi.order_id = o.id
GROUP BY c.customer_id, c.name
ORDER BY lifetime_revenue DESC
LIMIT 10;

PostgreSQL — aisql prompt: Show average session duration by device for last 7 days

SELECT
  device,
  ROUND(AVG(session_duration_seconds)::numeric, 2) AS avg_session_sec
FROM analytics.sessions
WHERE started_at >= now() - interval '7 days'
GROUP BY device
ORDER BY avg_session_sec DESC;

Generate SQL for aisql instantly with AI2sql — no technical expertise required.

Best Practices & Limitations

• Be explicit with metrics and filters. Spell out date ranges, statuses, and units to avoid ambiguity.

• Provide your schema or sample DDL. Accurate joins and column names make aisql outputs immediately runnable.

• Name your engine upfront. Dialects differ (date functions, identifiers, JSON ops). AI2sql targets MySQL, PostgreSQL, BigQuery, Snowflake, and more.

• Review before running on production. Use AI2sql as a SQL validator and formatter to catch edge cases and style issues.

• Iterate with variants. Ask for window vs. aggregate versions, or request performance-focused rewrites with indexes and filters.

• Handle sensitive data carefully. Mask or restrict PII and scope queries appropriately.

Tip: If you work in Postgres, see the PostgreSQL integration steps to connect your warehouse and generate queries in place.

Try aisql with AI2sql (inline demo CTA)

1. Open AI2sql and choose your database dialect.

2. Paste a snippet of your schema (tables, keys, key columns).

3. Describe your goal in plain English, for example: ‘Top 10 customers by revenue this quarter, include country.’

4. Generate SQL, review the explanation, then copy, run, or refine with variants.

Generate SQL for aisql instantly with AI2sql — no technical expertise required.

AI2sql also supports explain, validate, and format modes, so you can paste an existing query and get a human-readable explanation or a cleaner, dialect-safe version.

Conclusion

aisql removes friction between business questions and reliable answers. With AI2sql, you describe the outcome in plain English, provide minimal schema context, and get dialect-correct, production-ready SQL plus explanations and variants. Whether you are a PM requesting quick aggregates or a data engineer standardizing query style at scale, AI2sql speeds up every step. Connect your engine, generate queries, and iterate confidently with built-in validation and formatting. Ready to turn prompts into results?

Try AI2sql Free – Generate aisql Solutions