AI SQL generator

Give your SQL assistant context worth using.

TTSQL combines language-model generation with project schemas, Knowledge, and validation. It gives analysts and developers a SQL draft and a workflow for checking it.

Start free See examples

prompt

List customers that have never placed a paid order.

generated SQL PostgreSQL

1SELECT c.id, c.name2FROM customers AS c3WHERE NOT EXISTS (4  SELECT 1 FROM orders AS o5  WHERE o.customer_id = c.id AND o.status = 'paid'6)7ORDER BY c.id;

What the AI SQL generator uses

A generic SQL prompt may have no knowledge of your column names, relationships, or business definitions. A TTSQL project keeps that context close to the request. Describe important tables, document metrics, and add manually reviewed examples to guide generation.

Validation and correction loops can catch some invalid or unsafe drafts and retry with feedback. They cannot prove the business meaning of the result. Human review remains part of the workflow.

Evaluate an AI SQL tool on your own questions

Build a small evaluation set from questions you already know how to answer. Include ordinary lookups, joins, date boundaries, and ambiguous metrics. Compare the returned rows and totals against independently checked results.

Track failures by cause: wrong column, wrong join, missing exclusion, incorrect date window, or mistaken business definition. Improve the schema or Knowledge entry that addresses the cause, then rerun both the failed case and related cases.

Schema grounding: does the draft use real columns and valid relationships?
Meaning: does it answer the exact requested metric and population?
Review: can a user see and change the SQL before it runs?
Integration: does the API make generation and execution behavior explicit?

Keep approved knowledge deliberate

TTSQL's answer threads keep follow-ups and execution evidence together. An executed question/SQL pair becomes project Knowledge only through manual approval. A correction is not automatically a trusted example.

Use database permissions as a separate access boundary. Project context and model instructions help guide a query; they do not replace a database role with appropriately limited privileges.

Query examples

Prompts mapped to concrete SQL.

01 / PostgreSQL

Check an exclusion

List customers that have never placed a paid order.

Sample schema

customers(id PRIMARY KEY, name); orders(id, customer_id, status)

SELECT c.id, c.name
FROM customers AS c
WHERE NOT EXISTS (
  SELECT 1 FROM orders AS o
  WHERE o.customer_id = c.id AND o.status = 'paid'
)
ORDER BY c.id;

NOT EXISTS includes customers with no orders and customers whose orders are all unpaid. ‘Never’ differs from ‘not this month’; the prompt and SQL must agree on that scope.

Answers before you connect a database.

How accurate is TTSQL's AI-generated SQL?

Accuracy depends on the schema, definitions, question, and model output. Test with known answers, inspect SQL, and review results. TTSQL does not publish an accuracy guarantee for an unseen database.

Does validation guarantee that a query is correct?

No. A query can pass syntax and static checks while using the wrong business definition or join. Verify the returned data against the intended question.

Bring your next question to TTSQL.

Start with one PostgreSQL project and 20 free requests a day. Explore your data, build a report, or integrate the API.

Start free Contact sales