Glossary
The vocabulary l0l1 uses for LLM-assisted SQL, validation, and privacy at the warehouse boundary.
- Schema-aware validation
- Checking a query against the real tables, columns, and relationships of the connected database, rather than against generic SQL grammar alone.
- “Mostly correct” SQL
- LLM-generated SQL that is locally plausible and syntactically valid but silently wrong against your actual schema or semantics — l0l1’s core reason to exist.
- PII detection
- Scanning prompts and query text for personally identifiable information (SSNs, emails, phone numbers, card numbers) using Microsoft Presidio plus regex rules.
- Anonymization
- Replacing detected PII literals with placeholder tokens before a query is stored or forwarded to an external model provider.
- Presidio
- Microsoft’s open-source data protection and PII detection SDK, which l0l1 uses as the backbone of its scanner.
- Pattern learning
- Recording successful queries — sanitized of literals — as reusable shape patterns scoped to a workspace, so approved SQL can be suggested later.
- Shape pattern
- A query stripped of its specific literals, capturing structure (tables, columns, joins, filters) without the sensitive values.
- Workspace
- The scope within which l0l1 stores and surfaces learned query patterns, so one team’s approved SQL informs completions for that team.
- The four boundaries
- User-to-tool, tool-to-model-provider, tool-to-warehouse, and warehouse-to-output — the crossings l0l1 reasons about and protects.
- LLM reviewer
- The step where l0l1 uses OpenAI or Anthropic to reason about a query’s intent and correctness — reviewing SQL rather than authoring it.
- Schema introspection
- Reading and caching a database’s structure so validation has an accurate, up-to-date picture of what exists.
- LSP server
- The Language Server Protocol backend that powers l0l1’s VS Code extension, delivering validation and completions inside the editor.