How to earn stakeholder trust with evals and observability

In the context of evaluating AI features, data is dispersed across various platforms, necessitating centralized and comprehensible reporting for stakeholders. Braintrust offers three main tools for sharing evaluation and observability data: dashboards, custom trace views, and Loop. Dashboards provide aggregated visibility into metrics such as cost, latency, token usage, and eval scores, and can be tailored with time series, top lists, and big number charts for quick leadership reviews. Custom trace views transform complex AI interaction traces into accessible formats for non-technical stakeholders, fostering a clearer understanding of individual interactions. Loop acts as an AI assistant that translates natural language questions into SQL queries, allowing for on-the-fly data exploration and chart generation without engineering intervention. These tools collectively aim to break down silos, offering a cohesive and insightful view of AI performance and facilitating informed decision-making across engineering, leadership, and product management teams.

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