Prompt engineering acts a key skill in generative-AI. Precise and

This blog delves into Retrieval-Augmented Generation (RAG), a cutting-edge AI model that improves huge language models with real-time data retrieval for greater accuracy and relevance. It describes RAG’s design, practical applications, and implementation with Azure’s AI services, such as Azure AI Enrichments, Prompt Flow, Open AI, and custom solutions.

Generative Artificial Intelligence (AI) is currently a buzzword in the

Microsoft’s HoloLens 2 leads the way in mixed reality technology, enabling revolutionary holographic experiences. This transformative headset is reshaping industries from healthcare to marketing by merging virtual and real worlds. With a wide field of view and precise tracking, HoloLens 2 brings future potential to diverse sectors through immersive, collaborative and productive applications.

Generative AI creates new data from existing datasets, enabling automation across business functions like sales, marketing, HR, and legal. It powers applications like proposal generation, predictive analytics, document creation, and chatbots. Building a conversational chatbot involves steps like selecting a model, training it on curated data, iterating for accuracy, choosing a platform, and extensively testing the interface.