Data quality is a cornerstone for integrating large language models (LLMs) into organizations. The adage "garbage in, garbage out" holds particularly true here. High-quality data is the lifeblood that ensures the accuracy, relevance, and reliability of the model's outputs. In a business context, this translates to insights and decisions that
Tag: large language models
While large language models (LLMs) have become synonymous with advanced AI capabilities, their integration into various business and technological domains is often accompanied by significant costs. These costs arise from the extensive computational resources required for training and running these models. However, traditional natural language processing (NLP) techniques offer a
SAS' Julia Moreno shows you how to use generative AI to build a digital assistant that interacts with a model using natural language conversation.
Most people associate generative AI (GenAI) with large language models (LLMs). While LLMs focus specifically on generating text, GenAI encompasses a wider range of content generation tasks beyond just language, including images, music and more. Broadly speaking, GenAI uses machine learning algorithms to analyze and learn from existing data sets
Large language models (LLMs) are at the forefront of today’s AI, not merely as technological marvels but as transformative agents reshaping how businesses innovate, operate and deliver value. Think of them as the wizards of words, capable of understanding language and transforming it in ways that benefit organizations. However, as
In a global economy marked by fragile supply chains, scarce resources and rising energy costs, the spotlight is on forecasting to address these issues. In 2022, McKinsey & Company uncovered a staggering $600 billion annual food waste, equating to 33% – 40% of global food production, spotlighting the devastating consequences
SAS' Ali Dixon and Mary Osborne reveal why a BERT-based classifier is now part of our natural language processing capabilities of SAS Viya.