In this episode of Smart Enterprises: AI Frontiers, we explore a groundbreaking approach to improving large language model (LLM) performance by selecting high-quality pretraining data using perplexity correlations. We delve into the research that demonstrates how measuring the correlation between LLM losses and downstream benchmark performance can help businesses optimize pretraining data without the need for costly retraining. Join us as we unpack this efficient method and its potential to revolutionize the way enterprises select and refine data for AI models.