Is Data Curation the New Feature Engineering? (www.elicited.blog)

A recent exploration in AI Engineering draws an intriguing parallel between feature engineering in traditional machine learning (ML) and data curation in modern AI systems, particularly with frameworks like DSPy and GEPA. The article argues that, just as feature engineering shapes what a model can learn by transforming raw data into useful representations, data curation influences what an optimizer can explore. The author highlights that while many aspects of the optimization process can be automated, data curation requires nuanced human input and understanding of the problem domain, emphasizing its role in defining the optimizer’s search space. The significance of this insight lies in the realization that the effectiveness of AI models, particularly in the context of gradient-based optimization, hinges on how well the data is curated. Poor data curation can leave critical failure modes unaddressed, akin to having inadequate features in traditional ML, which blinds the model to essential patterns. As such, the author posits that good data curation could be the key to optimizing performance in AI, much like effective feature engineering does for traditional ML models. This perspective not only highlights the evolving role of human expertise in AI but also encourages practitioners to invest more effort into curating high-quality datasets that reflect diverse scenarios and complexities.