Product data and information architecture experts, Dave Skrobela, from Earley Information Science, walks through the elements of successful AI programs, the role of data quality and data architecture, and the best way to prioritize AI applications.
Product data and information architecture experts, Dave Skrobela, from Earley Information Science, walks through the elements of successful AI programs, the role of data quality and data architecture, and the best way to prioritize AI applications.
Artificial Intelligence is changing the way businesses interact with their customers. From hyper-personalized experiences to chatbots built on Large Language Models, AI is driving new investment in digital experiences. That same AI and LLM can also be used to automate your product data program. From data onboarding and validation to generating descriptions and validating images, AI can help generate content faster and at a higher quality level to improve product findability, search, and conversion rates. In our second webinar in the Product Data Mastery series, we’re speaking with Madhu Konety from IceCream Labs to show exactly how AI and product data can work together for your business.
By Dan O'Connor, Director of Product Data, Earley Information Science
What is Generative AI? Generative AI has caught fire in the industry – almost every tech vendor has a ChatGPT-like offering (or claims to have one). They are claiming to use the same technology – a large language model (LLM) (actually there are many Large Language Models both open source and proprietary fine-tuned for various industries and purposes) to access and organize content knowledge of the enterprise. As with previous new technologies, LLMs are getting hyped. But what is generative AI?
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