PostgreSQL Query Generator

How to generate SQL for your PostgreSQL schema

A useful query generator needs more than a database name. Give it the actual schema and a clear definition of the requested result. For example, “latest order” can mean the most recently created order, the latest paid order, or the latest version of an order. Those produce different SQL.

1. Connect an approved database. Use the connection instructions and a dedicated role restricted to the reporting tables or views you intend to query. Confirm the right database and schema are visible. For network, TLS and connection troubleshooting, use the PostgreSQL chatbot setup guide.
2. Supply the missing business context. Name important relationships, status meanings, currency, reporting time zone and row grain. Add approved definitions and SQL examples using training notes. A foreign key does not explain whether revenue excludes refunds or which timestamp determines the reporting period.
3. Ask for a bounded result. Specify output columns, filters, grouping and sort order. Ask the assistant to identify assumptions. Request a small sample first, then compare the generated query and returned rows with a known answer.
4. Refine and reconcile. Use a follow-up to change a filter or explain a join. Recheck totals after each change. A chart is useful only after the underlying rows and metric definition are correct.

A prompt you can adapt

Using reporting.orders, return one row per customer with their latest order ID and status. Latest means greatest created_at; break ties with the greatest order_id. Include cancelled orders. Show the SQL and explain the ordering. Do not invent tables or columns that are missing from the schema.

Replace the table and column names with yours. If you need broad syntax across several engines, start with the AI SQL query generator overview. This page focuses on PostgreSQL query construction; the connection guide covers chatbot setup and deployment.

Three PostgreSQL query examples with known results

Copy each complete SQL block into a test SQL session. The VALUES clauses provide fictional data, so the examples do not need your production tables. We executed these reference queries in local PGlite 0.5.8, based on PostgreSQL 18.3, and checked the results shown below. This validates these examples only: no AI product was benchmarked and no production database was queried.

WITH orders(customer_id, order_id, created_at, status) AS (
  VALUES
    (1, 101, TIMESTAMPTZ '2026-09-01 09:00+00', 'paid'),
    (1, 102, TIMESTAMPTZ '2026-09-02 09:00+00', 'paid'),
    (1, 103, TIMESTAMPTZ '2026-09-02 09:00+00', 'cancelled'),
    (2, 201, TIMESTAMPTZ '2026-09-01 10:00+00', 'paid')
)
SELECT DISTINCT ON (customer_id)
  customer_id, order_id, status
FROM orders
ORDER BY customer_id, created_at DESC, order_id DESC;

[
  {
    "customer_id": 1,
    "order_id": 103,
    "status": "cancelled"
  },
  {
    "customer_id": 2,
    "order_id": 201,
    "status": "paid"
  }
]

WITH customers(customer_id) AS (VALUES (1), (2), (3)),
orders(order_id, customer_id, status, amount_cents) AS (
  VALUES (101, 1, 'paid', 12000),
         (102, 1, 'pending', 7000),
         (103, 1, 'paid', 3000),
         (201, 2, 'cancelled', 9000)
)
SELECT c.customer_id,
  COUNT(o.order_id) FILTER (WHERE o.status = 'paid') AS paid_orders,
  COALESCE(SUM(o.amount_cents) FILTER (WHERE o.status = 'paid'), 0)
    AS paid_cents
FROM customers c
LEFT JOIN orders o ON o.customer_id = c.customer_id
GROUP BY c.customer_id
ORDER BY c.customer_id;

[
  {
    "customer_id": 1,
    "paid_orders": 2,
    "paid_cents": 15000
  },
  {
    "customer_id": 2,
    "paid_orders": 0,
    "paid_cents": 0
  },
  {
    "customer_id": 3,
    "paid_orders": 0,
    "paid_cents": 0
  }
]

WITH orders(order_id, attributes) AS (
  VALUES
    (101, '{"channel":"web","tags":["priority","gift"]}'::jsonb),
    (102, '{"channel":"web","tags":["gift"]}'::jsonb),
    (103, '{"channel":"store","tags":["priority"]}'::jsonb),
    (104, '{"channel":"web"}'::jsonb)
)
SELECT order_id
FROM orders
WHERE attributes ->> 'channel' = 'web'
  AND attributes @> '{"tags":["priority"]}'::jsonb
ORDER BY order_id;

[
  {
    "order_id": 101
  }
]

These examples illustrate PostgreSQL-specific choices rather than interchangeable SQL. The official documentation explains SELECT and DISTINCT ON, aggregate behavior and JSONB operators. Check the documentation for your deployed PostgreSQL version.

Review correctness, permissions and performance separately

A query can execute successfully and still answer the wrong question. Start with a manually reconciled case, then change a boundary condition. In the first example, the tie-break must select order 103. In the second, customers with no paid orders must remain. In the third, removing the priority tag must produce no match.

For a repeatable evaluation, use the text-to-SQL acceptance test kit. It supplies a separate local reference fixture and a scorecard; adapt its SQL to your engine. The natural-language database query guide explains how to turn business definitions into checkable questions.

Query generation is not a performance guarantee

An AI can suggest a rewrite, but indexes, statistics, table size and data distribution determine whether it helps. Review PostgreSQL’s EXPLAIN guidance. EXPLAIN ANALYZE executes the statement, including side effects of writes. Use an approved test environment and resource limits when checking runtime. Do not interpret a syntactically valid query or a faster-looking rewrite as a measured optimization.

Choose the workflow that matches the task

A visual query builder selects tables, columns and joins through controls; a natural-language generator starts from a question. AskYourDatabase focuses on connected conversational analysis. A synthetic-data generator creates test rows, and a dedicated tuning tool investigates query plans: those are different tasks. For Desktop analysis, start with an approved reporting dataset. For customer-facing analytics, review the embedded chatbot workflow and test access separately.

Desktop database queries run from your computer, while external AI processing may receive schema, questions and relevant results. Review security and data flow and private deployment options for the actual environment. A local database connection alone is not an offline AI guarantee.

Frequently asked questions