Unverified50% confidenceFactExact time
The mainstream implementation path for NL2SQL is to feed the database schema as context to a large language model, which infers user intent and generates SQL statements.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/8/2026
First Seen
Valid until: 1/6/2027
Sources
Related Entities
Related Claims
UnverifiedText-to-SQL 系统架构通常包含自然语言理解、Schema Linking 和 SQL 生成三个核心模块80% similarVerifiedSQL is a classic example of a declarative language, where users declare what data they want and the database query optimizer decides the optimal execution plan on its own.76% similarUnverified在NL2SQL场景中，为Schema的每个表和字段附加业务语义描述并标明表间关联关系，比简单堆砌CREATE TABLE语句效果更好74% similarUnverifiedWhen enterprises implement NL2SQL, they typically need to inject database schemas, field annotations, and common business glossaries into the prompt as context, or retrieve relevant schema fragments via RAG to improve generation accuracy.74% similarUnverifiedThe practical effectiveness of NLQ is highly dependent on the degree of underlying data standardization; the clearer the metadata, the more accurate the SQL generated by large language models.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/990046API
curl https://kongchang.com/api/v1/knowledge/claims/990046MCP
get_claim(id=990046)