Quality Control in Unstructured Data Systems

Establishing validation tests is essential to ensure that systems behave as expected, particularly in validating the performance of Generative AI applications against known benchmarks. While there are practices in place for structured data, the landscape for quality control in unstructured data remains underdeveloped. Concepts like chunking, embedding, and the use of vector databases bring new challenges that require innovative methodologies, particularly for managing and ensuring quality in anomaly detection. There is currently a significant gap in best practices within this field, highlighting an area ripe for exploration and development in the coming years.