Discover common RAG failure modes and how to fix them. Learn how to optimize retrieval-augmented generation systems for max business value.

Learn about large language model (LLM) alignment and how it maximizes the effectiveness of AI outputs for organizations.

Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how.

To tackle generative AI use cases, Snorkel AI + AWS launched an accelerator program to address the biggest blocker: unstructured data.

AI alignment ensures that AI systems align with human values, ethics, and policies. Here’s a primer on how developers can build safer AI.

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

We’re excited to announce Snorkel Custom to help enterprises cross the chasm from flashy chatbot demos to real production AI value.

Snorkel AI will be at Google Cloud Next. The event will feature more than 700 sessions, so we picked five that we think you shouldn’t miss.

Snorkel AI helped a client solve the challenge of social media content filtering quickly and sustainably. Here’s how.

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

Enterprise GenAI 2024: applications will likely surge toward production, according to Snorkel AI Enterprise LLM Summit survey results .

LLM distillation isolates task-specific LLM performance and mirrors it in a smaller format—creating faster and cheaper performance.

Snorkel CEO Alex Ratner talks with QBE Ventures’ Alex Taylor about the future of AI, LLMs and multimodal models in the insurance industry.

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.