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Lessons from building an AI data analyst
Text-to-SQL is not enough. Real user questions require multi-step plans, Python, and external context. A semantic layer encodes business meaning and sharply reduces SQL complexity.
Encodes business meaning: All the important context – dimensions, measures, relationships, and constraints – lives in a maintained semantic model instead of being buried inside prompts. Our application can retrieve them and pass them into the LLM's context, so the model knows, for instance, that " total_revenue" means "sum of price × quantity in USD" without having to infer it purely from the name. For instance, if certain measures are tagged as currency in USD or a trading_day dimension delineates business days vs. weekends, the assistant can incorporate that knowledge (maybe by calling a convert_currency() helper or using a pre-defined trading-days list) in the Python code it writes.
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