GenBI — generative Business Intelligence — lets anyone ask a question of their data in natural language and get back an answer, a table or a chart. The promise is strong; so is the danger: an LLM can produce a plausible but wrong answer. For decision-making, that is unacceptable. Here is how to frame GenBI.
Do not let the LLM invent the numbers
The LLM must not “know” your data: it must generate a query (SQL or a call to a semantic layer) executed by the engine, which alone returns the figures. The golden rule: the model translates intent, the database provides the truth.
A semantic layer as a guardrail
Defining metrics, dimensions and calculation rules upfront (what is “net revenue”?) prevents every question from reinventing the business logic. The semantic layer bounds the space of possible answers and guarantees consistency from one user to the next.
Make every answer verifiable
- Show the generated query and the sources used.
- Allow the question to be replayed and return the same result (determinism).
- Flag explicitly when a question falls outside the covered scope.
Measure reliability, not just usage
A reference set of questions with expected answers lets you track accuracy over time and on every change (model, schema, prompt). Without that measure, you deploy blind.
Discover our approach in the GenBI offer and our guides & resources.