LLM-assisted SQL review
For: Analytics engineers and data teams using LLMs to draft or fix SQL
Engineers increasingly reach for an LLM to write or fix a query, then paste the result into a dashboard, a job, or a warehouse console. The SQL usually looks right — and is sometimes silently wrong. l0l1 puts a review step in that loop.
The problem
A model returns confident SQL with a wrong join key or a column that does not exist on your schema. It passes a glance, gets shipped, and returns wrong numbers until someone notices weeks later.
How l0l1 helps
l0l1 validates the query against the live schema of the connected database and uses an LLM reviewer (OpenAI or Anthropic) to reason about intent. Problems surface before the query runs — in the CLI, the REST API, a notebook, or the editor.
In this workflow
- 1
A person or an LLM produces a query.
- 2
l0l1 validates it against the real columns and relationships of the database.
- 3
An LLM reviewer flags intent mismatches and likely errors.
- 4
Only a query that passes review is executed.