Recent advancements in Large Language Models and Retrieval-Augmented Generation have boosted interest in domain-specific question-answering for enterprise products. However, AI Assistants often face challenges in multi-product QA settings, requiring accurate responses across diverse domains. Existing multi-domain RAG-QA approaches either query all domains indiscriminately, increasing computational costs and LLM hallucinations, or rely on rigid resource selection, which can limit search results. We introduce MKP-QA, a novel multi-product knowledge-augmented QA framework with probabilistic federated search across domains and relevant knowledge. This method enhances multi-domain search quality by aggregating query-domain and query-passage probabilistic relevance. To address the lack of suitable benchmarks for multi-product QAs, we also present new datasets focused on three Adobe products: Adobe Experience Platform, Target, and Customer Journey Analytics. Our experiments show that MKP-QA significantly boosts multi-product RAG-QA performance in terms of both retrieval accuracy and response quality.

These technologies are engaging you--a human driving a car--in human terms. Myriad technologies that detect physical states such as alertness are increasingly being used to infer emotional states such as happiness or sadness. Unlike their machine forebears that set rigid rules of engagement, these systems will follow rules, reading your mood, intuiting your needs, and responding in contextually and emotionally appropriate ways. Welcome to the next stage of human-machine interaction, in which a growing class of AI-powered solutions--referred to as "affective computing" or "emotion AI"--is redefining the way we experience technology. These experiences are hardly confined to automobiles. Retailers are integrating AI-powered bots with customer segmentation and CRM systems to personalize customer interactions while at the same time capturing valuable lead-nurturing data.2 Apps are designing custom drinks and fragrances for fashion-show attendees based on emotional quotient (EQ) inputs.3 A global restaurant chain is tailoring its drive-through experiences based on changes in the weather.4

This blog originally published on Sitecore's "The Mind in the Machine" blog series. So what is AI--artificial intelligence? I think it's useful--at the risk of oversimplification--to state it this way: AI is machines that think and act like people. What that really means is that they need to understand and interpret data. To improve the customer experience, AI needs to be capable of processing data of various types--both structured and unstructured.

The corporate training market is over $200 billion around the world[1] and it's going through a revolution. While we often think of training as programs or courses, a new paradigm has arrived, one I call "Learning in the Flow of Work." The corporate training industry has been around for decades and it has always been impacted by new technology. As the following chart shows, over the last 20 years we've been through four evolutions, each driven by technological and economic change. In the 1970s and 1980s, when I started my career, we learned in classrooms. The technology was slide projectors and "foils" (plastic laminated slides).