This article details how Datadog's teams used LLM Observability to enhance their natural language query (NLQ) agent for analyzing cloud costs. It covers the creation of a ground truth dataset, the challenges of evaluating AI-generated queries, and the implementation of a structured debugging process to identify and address errors.

Effective data quality evaluation is essential for making informed decisions and involves a six-step framework. By defining clear goals, ensuring appropriate data sources, identifying anomalies, and using data observability tools, individuals can enhance the trustworthiness of their data and avoid the pitfalls of poor data quality.