Why Labeled Data is Essential for Generative AI Success

Why Labeled Data Still Matters in Generative AI | AI in 5 with Snorkel Think labeled data is outdated in the era of generative AI? Think again. In this episode of AI in 5, Snorkel AI’s Elena Boiarskaia explains why high-quality, labeled data is still essential—even when building with large language models like GPT-4. From prompt engineering to RAG optimization, Elena walks through how production-grade AI systems depend on structured evaluation, SME feedback, and labeled datasets at every stage. You’ll learn: - Why “out-of-the-box” generative AI isn’t enough for enterprise use - Where and why labeled data is critical in GenAI workflows - How Snorkel embeds subject matter expertise into the AI development lifecycle - What “AI-ready data” really means for tuning, evaluation, and deployment Whether you're building copilots, automating workflows, or evaluating LLM performance, this is a quick, essential breakdown of why labeling is back—and more important than ever. Subscribe for more fast takes on enterprise AI: https://www.youtube.com/@SnorkelAI Learn more at https://snorkel.ai #GenerativeAI #LLM #EnterpriseAI #AIin5 #SnorkelAI