Generative AI enables unprecedented creativity, efficiency and decision-making capabilities across industries. But it comes with significant challenges such as accuracy, context awareness and the reliability of generated content. Retrieval-Augmented Generation (RAG) helps address these limitations by seamlessly integrating real-time retrieval of accurate, proprietary data into generative processes.
This whitepaper explores how combining RAG with multi-model semantic integration can amplify your AI capabilities, creating solutions that are accurate, contextually relevant and rich in data from structured, unstructured and semi-structured sources. The Progress Data Platform supports multi-model RAG, providing enterprises with a robust, scalable and governed foundation to manage and integrate diverse data sources.
Download the whitepaper to uncover:
• Benefits of multi-model semantics and RAG in overcoming traditional GenAI limitations
• Advantages of implementing multi-model RAG with the Progress Data Platform
• Real-world applications across finance, healthcare and e-commerce, and the tangible business benefits achieved
• Step-by-step guidance to successfully deploy multi-model RAG in your enterprise
Harness the full potential of generative AI—download the whitepaper today and start delivering innovative solutions and driving impactful business outcomes.