Connecting a large language model to a company's data warehouse for analysis can be a powerful tool, but it's prone to errors. One common issue is the LLM generating SQL that runs but returns incorrect figures due to unstated business logic, such as excluding refunds or normalizing currency. Another problem is the LLM providing a plausible narrative explanation for a revenue drop instead of a data-driven diagnostic breakdown. AI
IMPACT LLMs require careful integration with business logic and data definitions to provide accurate analytical insights, rather than just plausible narratives.
RANK_REASON The article discusses practical limitations and solutions for using LLMs in a specific business application (data warehousing), rather than a core AI development.
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