Resource library
Evaluating Coding Agent Capabilities with Terminal-Bench: Snorkel’s Role in Building the Next Generation Benchmark
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Benchtalks #1: Alex Shaw (Terminal-Bench, Harbor) – Building the Benchmark Factory
Building FinQA: An Open RL Environment for Financial Reasoning Agents
How one large financial institution used call center AI to inform customer experience management with real-time data.
Learn how Snorkel can programmatically help you create massive amounts of high-quality labeled training data in a matter of hours.
This release features new GenAI tools and Multi-Schema Annotation, as well as new enterprise security tools and an updated home page.
RAG is the first step in building LLM-powered AI applications for enterprise use cases.
Enterprises must evaluate LLM performance for production deployment. Custom, automated eval + data slices present the best path to production.
In this webinar, presented by Snorkel and Numbers Station, we’ll explain how to elicit data-driven insights backed by domain-knowledge using a multi-agent architecture and a fine-tuned LLM and/or RAG pipeline.
Meta’s Llama 3.1 405B, rivals GPT-4o in benchmarks, offering powerful AI capabilities. Despite high costs, it can enhance LLM adoption through fine-tuning, distillation, and as an AI judge.
Meta released Llama 3 405B today, signaling a new era of open source AI. The model is ready to use on Snorkel Flow.
High-performing AI systems require more than a well-designed model. They also require properly constructed training and testing data.
