AI Hallucinations in Text-to-SQL: Four Common Pitfalls and How to Mitigate Them

Large language models generate SQL by predicting tokens rather than truly understanding databases, leading to four categories of hallucinations—factual, logical, instructional, and knowledge‑boundary—each with concrete examples, and the article outlines five practical strategies such as schema‑pre‑alignment, execute‑then‑rerank, compiler feedback, real‑time schema sync, and human verification to curb these errors.

AI hallucinationRAGSQL validation

0 likes · 9 min read

AI Hallucinations in Text-to-SQL: Four Common Pitfalls and How to Mitigate Them

Wu Shixiong's Large Model Academy

Dec 15, 2025 · Artificial Intelligence

Mastering Text2SQL: From Schema Design to Secure Multi‑Step LLM Pipelines

This article explains how Text2SQL works by teaching LLMs to understand a closed‑world database schema, constructing tightly constrained prompts, validating generated SQL, handling execution errors, and using a second LLM call to translate results into natural language, while highlighting common pitfalls and engineering best practices.

LLMSQL validationschema design

0 likes · 9 min read

Mastering Text2SQL: From Schema Design to Secure Multi‑Step LLM Pipelines