Embracing Responsible AI: Measure and Mitigate Risks for a Generative AI App in Azure AI Studio
Artificial intelligence has taken the world by storm, redefining the way businesses operate and innovate. Whether you're an experienced developer or a beginner looking to break into the world of AI, Azure AI Studio offers a robust platform for creating cutting-edge AI applications responsibly and securely. I recently had the opportunity to dive into the Microsoft Learn module: Measure and Mitigate Risks for a Generative AI App in Azure AI Studio. It’s an incredible resource that walks you through every step of building and refining a responsible AI application. Today, I’d like to share my experience and encourage you to embark on this journey too, gaining essential skills in the process.Unlocking the Power of Your Data: Build Smarter Apps with AI
ChatGPT is cool, but what if YOU could harness AI on YOUR data? This guide reveals how LlamaIndex and Azure make it surprisingly easy to build powerful AI apps that answer questions, generate code, and more! Explore real-world use cases and discover how Azure samples simplify the process from start to finish.Why Should Business Adopt RAG and migrate from LLMs?
In this blog we are going to discuss the importance of migrating your product or startup project from LLMS to RAG. Adopting RAG empowers businesses to leverage external knowledge, enhance accuracy, and create more robust AI applications. It’s a strategic move toward building intelligent systems that bridge the gap between generative capabilities and authoritative information. Below are topics in this blog. Brief History of AI What are Large Language Models (LLMS). Limitation of LLMS. How can we incorporate domain knowledge. What is Retrieval Augmented Generation (RAG). What is Robust retrieval for RAG Apps. Once we are done with these concepts, I hope to convince you to adopt RAG in your project.Building your own copilot – yes, but how? (Part 2 of 2)
With the term copilot we refer to a virtual assistant solution hosted in the Cloud, using an LLM as a chat engine, which is fed with business data and custom prompts and eventually integrated with 3 rd party services and plugins. In the first blog of this series, we covered how to build a copilot on custom data using low code tools and Azure out-of-the-box features. In this blog post we’ll focus on developer tools and code-first experience.
