{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/text-to-sql",
  "headline": "Text-to-SQL: Natural Language Database Querying with Large Language Models",
  "description": "Text-to-SQL (NL2SQL) translates natural language questions into executable SQL queries, enabling anyone in an organization to query databases without knowing SQL. With LLMs, the technology has moved from lab benchmarks to production deployments, handling complex multi-table joins, nested subqueries, and domain-specific business logic from plain English questions.",
  "dateCreated": "2026-05-24T02:49:13.667Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "A robust natural language text-to-SQL generation framework leveraging large language models",
      "sameAs": "https://www.nature.com/articles/s41598-026-39128-9"
    },
    {
      "@type": "CreativeWork",
      "name": "Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Database Semantic Parsing",
      "sameAs": "https://arxiv.org/abs/1809.08887"
    }
  ]
}