Resource library
Explore our complete library of resources including blogs, benchmarks, research papers, and more.
Why coding agents need better data, evals, and environments
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Evaluating coding agent capabilities with Terminal-Bench: Snorkel’s role in building the next generation benchmark
Building FinQA: An Open RL Environment for Financial Reasoning Agents
The science of rubric design
Announcing two new products on our AI Data Development Platform that together create a complete solution for enterprises to specialize AI systems with expert data at scale.
Experian’s customer support team used to require agents to manually find and customize templates to respond to common questions.
Enterprises facing aggressive revenue targets without more headcount are turning to agentic AI innovator Rox. Rox is redefining the revenue stack with it’s AI-powered sales productivity platform, starting with the Rox sales agent swarm which provides agents that can perform at the level of top sales reps.
Our client values customer satisfaction as a core business goal, but their ability to track it with their virtual assistant was limited. The company typically tried to collect experience ratings at the end of interactions. Customers answered these surveys infrequently—and typically when dissatisfied—making the data unreliable in its raw form.
In this webinar, learn how leading AI teams use Snorkel GenAI evaluation to accelerate the process by combining programmatic evaluation with SME-in-the-loop workflows, identify where and why failures occur within your business context, and establish reliability and trust through validation.
Discover how enterprises can leverage LLM-as-Judge systems to evaluate generative AI outputs at scale, improve model alignment, reduce costs, and tackle challenges like bias and interpretability.
It’s critical enterprises can trust and rely on GenAI evaluation results, and for that, SME-in-the-loop workflows are needed. In my first blog post on enterprise GenAI evaluation, I discussed the importance of specialized evaluators as a scalable proxy for SMEs. It simply isn’t practical to task SMEs with performing manual evaluations – it can take weeks if not longer, unnecessarily…
We’re taking a look at the research paper, LLMs can easily learn to reason from demonstration (Li et al., 2025), in this week’s community research spotlight. It focuses on how the structure of reasoning traces impacts distillation from models such as DeepSeek R1. What’s the big idea regarding LLM reasoning distillation? The reasoning capabilities of powerful models such as DeepSeek…
GenAI needs fine-grained evaluation for AI teams to gain actionable insights.
