Choosing the right large language model (LLM) for your use case is becoming both increasingly challenging and essential. Many teams rely on one-time (ad hoc) evaluations based on limited samples from trending models, essentially judging quality on “vibes” alone.
This approach involves experimenting with a...
This guest post was written by Mulay Ahmed and Caroline Lima-Lane of Principal Financial Group. The content and opinions in this post are those of the third-party authors and AWS is not responsible for the content or accuracy of this post.
With US contact centers...
This post is co-written with Ross Ashworth at TP ICAP.
The ability to quickly extract insights from customer relationship management systems (CRMs) and vast amounts of meeting notes can mean the difference between seizing opportunities and missing them entirely. TP ICAP faced this challenge, having...
Organizations often face challenges when implementing single-shot fine-tuning approaches for their generative AI models. The single-shot fine-tuning method involves selecting training data, configuring hyperparameters, and hoping the results meet expectations without the ability to make incremental adjustments. Single-shot fine-tuning frequently leads to suboptimal results...
Multimodal fine-tuning represents a powerful approach for customizing vision large language models (LLMs) to excel at specific tasks that involve both visual and textual information. Although base multimodal models offer impressive general capabilities, they often fall short when faced with specialized visual tasks, domain-specific...
Artificial Intelligence (AI) is transforming the quick-service restaurant industry, particularly in drive-thru operations where efficiency and customer satisfaction intersect. Traditional systems create significant obstacles in service delivery, from staffing limitations and order accuracy issues to inconsistent customer experiences across locations. These challenges, combined with...
