Beyond Text-to-SQL: Building an AI Data Analyst
This article explores the challenges and solutions in building an AI data analyst. The author argues that simple text-to-SQL is insufficient for real-world user questions, requiring multi-step plans, external tools (like Python), and external context. Their team built a generative BI platform using a semantic layer powered by Malloy, a modeling language that explicitly defines business logic. This, combined with a multi-agent system, retrieval-augmented generation (RAG), and strategic model selection, achieves high-quality, low-latency data analysis. The platform generates SQL, writes Python for complex calculations, and integrates external data sources. The article stresses context engineering, retrieval system optimization, and model selection, while sharing solutions for common failure modes.