Scaling Data Quality in the Era of LLMs: A Quality Framework

As Large Language Models and AI agents advance in capability and autonomy, an often-overlooked challenge emerges: their success hinges on access to high-quality training data that traditional quality assurance processes struggle to deliver at scale. This dependency creates a critical bottleneck—conventional QA methodologies cannot match the complexity and volume demands of modern AI systems, constraining innovation across the field. This session presents a hybrid quality assurance framework that combines intelligent project scoping, automated expert matching, and multi-layered validation through both AI agents and human oversight. We demonstrate how this integrated approach maintains data quality while scaling across diverse annotation projects, addressing key failure modes in traditional workflows. Through a case study using Toloka's self-service platform, we share design principles for building efficient and scalable data pipelines essential for training both LLMs and AI agents.