Chris Glaze

Part 3 of our rubric series explains the science of rubric design. We show why rubrics should be treated like models—structured, measured, and iterated—to maximize objective alignment and inter-rater agreement. Learn how to choose hierarchy and scale points, track agreement (IAA) and LLMAJ alignment, and refine with domain experts, with examples like PaperBench and HealthBench.

In this post, we unpack how Snorkel built a realistic benchmark dataset to evaluate AI agents in commercial insurance underwriting. From expert-driven data design to multi-tool reasoning tasks, see how our approach surfaces actionable failure modes that generic benchmarks miss—revealing what it really takes to deploy AI in enterprise workflows.

In this post, we will show you a specialized benchmark dataset we developed with our expert network of Chartered Property and Casualty Underwriters (CPCUs). The benchmark uncovers several model-specific and actionable error modes, including basic tool use errors and a surprising number of insidious hallucinations from one provider. This is part of an ongoing series of benchmarks we are releasing across verticals…

The Snorkel Flow label model plays an instrumental role in driving the enterprise value we create. Here’s a peek at how it works.

Snorkel takes a step on the path to enterprise superalignment with new data development workflows for enterprise alignment

Enterprises that aim to build valuable GenAI applications must view them from a systems-level. LLMs are just one part of an ecosystem.