Google and Snorkel AI customized PaLM 2 using domain expertise and data development to improve performance by 38 F1 points in a matter of hours.

Humans learn tasks better when taught in a logical order. So do LLMs. Researchers developed a way to exploit this tendency called “Skill-it!”

Fine-tuned representation models are often the most effective way to boost the performance of AI applications. Learn why.

Snorkel AI CEO Alex Ratner explains his view on the importance of AI in data development and illustrates his position with two case studies.

We’ve developed new approaches to scale human preferences and align LLM output to enterprise users’ expectations by magnifying SME impact.

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

Snorkel AI’s Jan. 25 Enterprise LLM Summit focused on one theme: AI data development drives enterprise AI success.

Snorkel researchers’ state-of-the-art methods created a 7B LLM that ranked 2nd, behind only GPT-4 Turbo, on AlpacaEval 2.0 leaderboard.

When done right, advanced classification applications cultivate business value and automation, unlock new business lines, and reduce costs.

A brief guide on how financial institutions could use Google Dialogflow with Snorkel Flow to build better chatbots for retail banking

A proof-of-concept project that combines predictive AI + generative AI to minimize LLM’s risks while keeping their advantages.

Large language models open many new opportunities for data science teams, but enterprise LLM challenges persist—and customization is key.

LLMs have a broad but shallow knowledge, but fall short on specialized tasks. For best performance, enterprises must fine tune their LLMs.

Distillation techniques allow enterprises to access the full predictive power of large language models at a tiny fraction of their cost.

Data labeling remains a core requirement for machine learning projects—especially in the age of genAI and LLMs. Here’s a handy guide.