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IBM TechXchange Dev Day: Virtual Agents
Join us 23 January from 11 AM - 6 PM ET as over 30 speakers from IBM and key AI industry leaders discuss the latest AI trends.
The release of watsonx.governance 2.0.1 includes several new features making it the best AI Governance solution for your customers. One highly requested feature is the ability to evaluate and monitor generative AI prompts for Retrieval Augmented Generation (RAG) use cases. UI enhancements and...
RAG (Retrieval Augmented Generation) has been revolutionizing conversational AI. This technique lets chatbots access and leverage external knowledge bases, i.e., your domain or real-time data, tailoring them to your needs. And that’s led to a boom in RAG chatbots! In this article, we’ll...
RAG (Récupération Augmentée de Génération) a révolutionné l'IA conversationnelle. Cette technique permet aux agents conversationnel d'accéder et d'exploiter des bases de connaissances externes, c'est-à-dire votre domaine ou des données en temps réel, en les adaptant à vos besoins. Cela a...
I'm thrilled to announce that I'll be speaking at the upcoming IBM TechXchange Conference at the MGM Grand in Las Vegas! Join me for an exciting session titled Be a Super Snooper: Use watsonx.governance to Monitor Your AI Wherever It Lives [1223] In this session, I'll walk...
I'm thrilled to announce that I'll be speaking at the upcoming IBM TechXchange Conference at the MGM Grand in Las Vegas! Join me for an exciting session titled "Be a Super Snooper: Use watsonx.governance to Monitor Your AI Wherever It Lives." In this session, I'll walk you through how...
Generative AI has gained widespread traction in recent times. Large Language models (LLM) are heavily used by both enterprises and individual users — whether it’s for generating new content, efficiently answering user queries with a personal touch, or summarising large documents, LLMs have...
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IBM watsonx.governance and AWS SageMaker integration for AI governance.pdf
Greetings! The Use Cas e Let’s consider a scenario where we are developing an LLM powered application using langchain, say for Mobiles Issues Summarization, further classifying the issue, and finally generate issue resolution for that issue type. So, in total 3 processing steps...