Azure AI announces Prompt Shields for Jailbreak and Indirect prompt injection attacks
Our Azure OpenAI Service and Azure AI Content Safety teams are excited to launch a new Responsible AI capability called Prompt Shields. Prompt Shields protects applications powered by Foundation Models from two types of attacks: direct (jailbreak) and indirect attacks, both of which are now available in Public Preview.Correction capability helps revise ungrounded content and hallucinations
Today, we are excited to announce a preview of "correction," a new capability within Azure AI Content Safety's groundedness detection feature. With this enhancement, groundedness detection not only identifies inaccuracies in AI outputs but also corrects them, fostering greater trust in generative AI technologies.
Azure OpenAI Best Practices Insights from Customer Journeys
When integrating Azure OpenAI’s powerful models into your production environment, it’s essential to follow best practices to ensure security, reliability, and scalability. Azure provides a robust platform with enterprise capabilities that, when leveraged with OpenAI models like GPT-4, DALL-E 3, and various embedding models, can revolutionize how businesses interact with AI. This guidance document contains best practices for scaling OpenAI applications within Azure, detailing resource organization, quota management, rate limiting, and the strategic use of Provisioned Throughput Units (PTUs) and Azure API Management (APIM) for efficient load balancing.
Intelligent Load Balancing with APIM for OpenAI: Weight-Based Routing
Weightage: There is no direct feature capablities in APIM for weightage based routing.I have tried achieve same results using custom logic with APIM policies Selection Process: Backend logic used in this policy is based on weighted selection method to choose an endpoint route for retry.endpoint with higher weights are more likely to be chosen, but each endpoints route has at least some chance of being selected. This is because the selection is based on a random number that is compared against cumulative weights, which means the selection process inherently favors routes with higher weights due to the way cumulative weights are calculated and utilized
Potential Use Cases for Generative AI
Azure’s generative AI, with its Copilot and Custom Copilot modes, offers a transformative approach to various industries, including manufacturing, retail, public sector, and finance. Its ability to automate repetitive tasks, enhance creativity, and solve complex problems optimizes efficiency and productivity. The potential use cases of Azure’s generative AI are vast and continually evolving, demonstrating its versatility and power in addressing industry-specific challenges and enhancing operational efficiency. As more organizations adopt this technology, the future of these sectors looks promising, with increased productivity, improved customer experiences, and innovative solutions. The rise of Azure’s generative AI signifies a new era of intelligent applications that can generate content, insights, and solutions from data, revolutionizing the way industries operate and grow.
