If you were to stop someone walking down the street and ask them how long artificial intelligence, or AI, has been a hot topic, they might say it’s something that’s emerged mostly in recent years. But AI has been around for a long time, with the term first being coined as long ago as 1955. Generative AI however is a different beast, and one that's largely responsible for moving the topic of AI to the tip of everyone’s tongues – from consumers to enterprises alike.

Data lakes can save your enterprise money but come with unique challenges. These tips will help you overcome three of the biggest issues.

Every company around the globe is trying to get in on the GenAI wave to simplify user experiences with natural language. And this is especially true in the realm of data and analytics. Imagine if you could enable all of your marketers to evaluate the performance of their campaigns with a simple question? Or, if you could provide all of your insurance risk managers with the ability to analyze the risk profile of their claims with the power of search and automated insights?

Organizations that monitor their LLMs will benefit from higher performing models at higher efficiency, while meeting ethical considerations like ensuring privacy and eliminating bias and toxicity. In this blog post, we bring the top LLM metrics we recommend measuring and when to use each one. In the end, we explain how to implement these metrics in your ML and gen AI pipelines.