A conversational BI tool that returns a confident, well-written paragraph with the wrong figure is worse than useless. It is dangerous, because the answer sounds correct.
The model is not the source of truth
Large language models are brilliant at producing fluent text and reasonable SQL, but they do not know your business definitions. Left to guess, they will happily average the wrong column or mix up currency. The accuracy of a generative BI answer is bounded by how well the system constrains the model to your certified logic.
The semantic layer as guardrail
A well-modeled semantic layer maps plain-language concepts to exact metrics, dimensions, and filters. When the query engine resolves intent against that layer before generating SQL, the model fills structure instead of inventing meaning. Ambiguity drops, and so do wrong-number incidents. The semantic layer is the difference between a toy and a tool.
Closing the loop
Even with a strong layer, monitor unanswered and corrected queries, capture the corrections back into the definitions, and show users the logic behind each answer. Transparency turns a black box into a trusted colleague, and every correction makes the next answer more accurate.
Key Takeaways
- An LLM does not know your definitions; it will guess if allowed.
- Resolve intent against a semantic layer before generating SQL.
- Capture corrections and show the logic to build trust.
Conclusion
Generative BI accuracy is an engineering problem, not a model problem. Get the semantic layer right and the natural language becomes a strength instead of a liability.