Organizations building and deploying AI applications, particularly those using large language models (LLMs) with Retrieval Augmented Generation (RAG) systems, face a significant challenge: how to evaluate AI outputs effectively throughout the application lifecycle. As these AI technologies become more sophisticated and widely adopted, maintaining...
This post was co-written with Vishal Singh, Data Engineering Leader at Data & Analytics team of GoDaddy
Generative AI solutions have the potential to transform businesses by boosting productivity and improving customer experiences, and using large language models (LLMs) in these solutions has become increasingly...
This post is cowritten with Harrison Hunter is the CTO and co-founder of MaestroQA.
MaestroQA augments call center operations by empowering the quality assurance (QA) process and customer feedback analysis to increase customer satisfaction and drive operational efficiencies. They assist with operations such as QA...
The Qwen 2.5 multilingual large language models (LLMs) are a collection of pre-trained and instruction tuned generative models in 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B (text in/text out and code out). The Qwen 2.5 fine tuned text-only models are optimized for multilingual dialogue...
The integration of generative AI agents into business processes is poised to accelerate as organizations recognize the untapped potential of these technologies. Advancements in multimodal artificial intelligence (AI), where agents can understand and generate not just text but also images, audio, and video, will...
Open foundation models (FMs) allow organizations to build customized AI applications by fine-tuning for their specific domains or tasks, while retaining control over costs and deployments. However, deployment can be a significant portion of the effort, often requiring 30% of project time because engineers...
