Data Mage: meet our AI data analyst that lives in Slack

In response to the repetitive and time-consuming nature of addressing data-related inquiries through Slack, a data team developed an innovative Slack chatbot integrated with BigQuery, Metabase, and a large language model (LLM) to facilitate sophisticated data conversations. Initially, the internal chatbot faced challenges with schema exploration and building upon past queries. However, after dedicating time to develop a more robust solution, the team created a chatbot that significantly improved user interaction, averaging 5.5 conversational turns per session and receiving positive feedback. The chatbot, built using PydanticAI and Claude, relies on structured prompts, governed tools, and a memory system to ensure accurate and reliable SQL query generation, while maintaining deterministic guardrails in the tools to prevent unwanted actions. The project highlights the importance of robust data documentation, the effectiveness of a governed metric layer, and the necessity of embedding guardrails within the tools rather than relying solely on prompts. As the tool matures, the team plans to implement an evaluation system to continually assess and improve the chatbot's accuracy in delivering correct data insights, recognizing the critical role of trust in data analysis.

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