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- How a global financial services company built a specialized AI copilot accurate enough for production
- Introduction A multinational, multibillion-dollar financial services company set out to develop an AI copilot which could provide call center agents with helpful answers to customer questions. The goal was to generate high-quality, low-latency responses without incurring significant inference costs as usage grows. However, ChatGPT 3.5 Turbo with RAG suffered from… ... - Task Me Anything: innovating multimodal model benchmarks
- "Task Me Anything" empowers data scientists to generate bespoke benchmarks to assess and choose the right multimodal model for their needs. ... - Alfred: Data labeling with foundation models and weak supervision - Introducing Alfred: an open-source tool for combining foundation models with weak supervision for faster development of academic data sets. ...
- RAG: Boost LLM performance with retrieval-augmented generation
- Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how. ... - Call center AI for customer experience management: a case study - How one large financial institution used call center AI to inform customer experience management with real-time data. ...
- New GenAI features, data annotation: Snorkel Flow 2024.R2 - This release features new GenAI tools and Multi-Schema Annotation, as well as new enterprise security tools and an updated home page. ...
- Meta’s Llama 3.1 405B is the new Mr. Miyagi, now what? - 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’s new Llama 3.1 models are here! Are you ready for it? - Meta released Llama 3 405B today, signaling a new era of open source AI. The model is ready to use on Snorkel Flow. ...
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