Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information.
In Depth
In AI, a hallucination occurs when a large language model generates content that appears plausible and coherent but is factually incorrect, fabricated, or unsupported by the training data or provided context. For database applications, hallucinations can manifest as references to non-existent tables or columns, incorrect SQL syntax, made-up data values, or logically unsound query constructions. Hallucinations are particularly dangerous in data analysis because incorrect queries can lead to wrong business decisions. Mitigation strategies include schema validation, query verification, RAG (grounding responses in actual schema data), output constraints, and human-in-the-loop review.
How AI for Database Helps
AI for Database minimizes hallucinations by validating all generated SQL against your actual schema before execution and flagging potential issues.
Related Terms
Large Language Model
An AI model trained on vast text data that can understand and generate human language, powering text-to-SQL and conversational AI.
RAG
Retrieval-Augmented Generation—an AI technique that enhances LLM responses by retrieving relevant context from external data sources.
Prompt Engineering
The practice of crafting effective instructions and context for AI models to produce desired outputs.
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