AI SQL Generator

Code

Natural language → dialect-correct SQL — with CTE style, comments, and safe patterns when you describe the schema truthfully.

Let schemas steer the model

Garbage schema → garbage joins.

Paste real DDL or trustworthy ERD notes — the generator binds identifiers to what you proved exists, refuses to invent columns, and surfaces assumptions in comments before the SQL itself. Default mutation policy is read-only so a Tuesday lunch query does not become a Friday resume-update event. Output is dialect-aware: PostgreSQL window functions, BigQuery STRUCTs, Snowflake QUALIFY clauses, SQLite quirks — each gets honest treatment without pretending one engine is another.

How to brief queries that survive code review

Five disciplines that beat "vibes-based SQL."

1. Pick the dialect honestly — Postgres-flavored SQL on BigQuery wastes everyone's afternoon.
2. Paste real DDL or column-level ERD notes; "there's a users table" is not enough context for safe joins.
3. Phrase the question with concrete predicates ("last 90 days," "excluding test accounts") instead of "recent active users."
4. Choose style — CTE-commented for review, minimal for ad-hoc, defensive when nullability is uncertain.
5. Set the mutation policy before generating; SELECT-only is the safe default for nearly all exploratory work.
6. Run EXPLAIN on big tables before executing; AI can write correct SQL that scans terabytes you didn't budget.

Style presets

Match query style to the downstream reader.

CTE + comments

Review-friendly

Common-table-expression scaffolding with line comments — easy for the next engineer to extend or audit.

Minimal

One-liner energy

Shortest correct query — useful for ad-hoc Slack-pasted lookups where verbosity is friction.

Defensive

Nullability paranoia

Explicit casts, COALESCE guards, and null-aware joins for messy production data.

Best for

Queries that benefit from structured assumptions.

• Analyst onboarding where the schema is large and tribal knowledge is missing
• Cross-dialect translation when migrating Postgres queries to BigQuery or Snowflake
• Quick metric prototyping before committing to a dbt model or BI definition
• Window-function patterns (lag/lead/rank) that you write rarely enough to forget
• Defensive queries against tables with known data quality issues
• Migration shape suggestions for new tables, indexes, and constraints
• Generating boilerplate JSON-extraction SQL for semi-structured columns

Why the assumption block matters more than the SQL

A query is only as correct as the schema you brief.

Most production SQL bugs are not syntax errors — they are unstated assumptions: a join key that is unique on one side but not the other, a timestamp in UTC vs local, an enum value that drifted last quarter. This template forces the model to surface those assumptions in comments before the SQL itself. If your schema context is incomplete, the assumption block tells you exactly where to fill in. Treat the assumptions as the deliverable; the SQL just falls out of them once they are correct.

Pro tips for safer generated SQL

Habits that compound across analyst teams.

1. Always EXPLAIN before running — generated SQL can be correct and catastrophically expensive at the same time.
2. Add real sample rows to your schema context when nullability or enum values matter to the query.
3. Use defensive style for any query that will run in production cron jobs.
4. Treat the review checklist at the end of every output as a real review checklist, not a formality.
5. When the model asks clarifying questions in comments, answer them in your next iteration, not by ignoring them.
6. Pair with the AI Data Analyzer to sanity-check the generated query results against pasted samples.

SQL Generator FAQ

Will the SQL run on my database?

Copy/paste into your SQL client after review — always EXPLAIN first on big tables. Generated SQL is correct against the schema you described, but execution depends on your engine version and data volume.

Will it invent columns or tables I didn't mention?

It is instructed not to. If you see an unfamiliar identifier in the output, your schema context is incomplete — fix it and regenerate rather than running the query.

Can it write destructive queries?

Default mutation policy is read-only (SELECT/WITH only). You can opt into INSERT or DDL-shape suggestions, but always treat those as drafts that need human review before running against production.

Does it understand my company's specific data model?

Only what you paste in the schema context. The richer your DDL, the better the SQL. Storing canonical schema snippets in a notes app accelerates future runs.

Will it optimize for performance?

It uses idiomatic patterns for the chosen dialect, but final optimization (indexes, partitioning, materialization) is your DBA's call. The output is a starting point, not a tuning recommendation.

Which models power it?

Default reasoning-capable models for SQL accuracy. Switch to a deeper model for complex multi-CTE analytics or window-function patterns.

How do I handle dynamic dialects (Postgres + Aurora + Citus)?

Pick the closest dialect in the dropdown and add specific extension or version notes in the schema context — "using Citus distributed tables" or "Aurora Postgres 15 with pg_partman."

Prototype queries fast, ship them safe

AI for scaffolding; humans for permissions and prod.

Use this to skip the boilerplate, get assumptions surfaced, and start with a correct first draft. Then read the assumption block, EXPLAIN the plan, and let your humans own the production deploy. Velocity in analytics comes from short loops, not from skipping review.