How your LLM is silently hallucinating company revenue

The New Stack

by Alasdair Brown

February 19, 2026

AI-Generated Deep Dive Summary

Large language models (LLMs) are revolutionizing development by automating tasks like generating React components, building backend APIs, and crafting SQL queries. However, while these tools excel at producing syntactically correct code, their semantic accuracy is far less reliable—especially when working with databases. This can lead to a dangerous failure mode where LLMs generate seemingly valid but fundamentally wrong queries that silently mislead users. The issue arises because database work is particularly susceptible to silent failures due to three key factors: SQL dialect variations, messy schemas, and ambiguous human communication. LLMs often struggle with nuanced differences between SQL dialects, leading them to produce incorrect syntax or logic. Additionally, real-world data schemas are rarely clean—columns like "amount" might refer to gross revenue, net revenue, or quantity, while tables named "users" and "customers" could overlap or diverge entirely. These ambiguities make it difficult for LLMs to accurately model relationships between data elements. The stakes are especially high when LLM-generated queries involve financial metrics, such as calculating revenue by product category. If the query incorrectly pulls data from the wrong table or misapplies conditions, it can return numbers that look valid but are entirely off. For example, a CFO might make critical business decisions based on "revenue" figures that actually come from order items instead of products. This kind of silent hallucination can lead to significant financial losses and erode trust in AI tools. For DevOps professionals, this means relying solely on LLMs for database tasks is risky. While these tools can accelerate development, they require careful oversight and validation—especially when handling critical business metrics. Without proper governance, the silent errors they generate could have far-reaching consequences for company revenue and decision-making.

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Originally published on The New Stack on 2/19/2026