Text to SQL uses LLMs to translate natural language into executable SQL, but its reliability often falls short of expectations. We’ve moved this blip to Hold to discourage its use in unsupervised workflows — for example, dynamically converting user-generated queries where the output is hidden or automated. In these cases, LLMs frequently hallucinate due to limited schema or domain understanding, risking incorrect data retrieval or unintended data modification. The non-deterministic nature of LLM outputs also makes debugging and auditing errors challenging.
We advise treating Text to SQL with caution, human review is required for all generated queries. For agentic business intelligence, avoid direct database access and instead use a governed data abstraction semantic layer — such as Cube or dbt's semantic layer — or a semantically rich access layer like GraphQL or MCP.
Text to SQL is a technique that converts natural language queries into SQL queries that can be executed by a database. Although large language models (LLMs) can understand and transform natural language, creating accurate SQL for your own schema can be challenging. Enter Vanna, an open-source Python retrieval-augmented generation (RAG) framework for SQL generation. Vanna works in two steps: first you create embeddings with the data definition language statements (DDLs) and sample SQLs for your schema, and then you ask questions in natural language. Although Vanna can work with any LLMs, we encourage you to assess NSQL, a domain-specific LLM for text-to-SQL tasks.
