Working with Generative AI is cool. As a user, you mostly see the chatbot as frontend of an AI application with amazing results produced by Large Language Models (LLMs). However, as a developer, I need more insights during the development phase. Specifically, I want to know what information is being passed to my LLM. That’s why I usually enable tracing for my AI applications, but how can I do this in Prompt flow? That is not so difficult. So let…
I began this journey into Application Lifecycle Management for AI applications by introducing ALM in AI Studio (Getting started with ALM in AI Studio). Moreover, I provided a brief overview of Azure AI Studio and the definition phase to you. Subsequently, we explored the Azure AI Studio Development Process in depth. I detailed how I developed my example solution. Today, I will proceed to discuss how I’m running a streamlined test process in Azure AI Studio for my AI solution as part…
Are you ready for the next steps? In my last blog post Getting started with ALM in AI Studio I described how-to define an AI use case in Azure AI Studio. I have written down my objective for my use case, prepared my sample data and created a basic prompt for my AI application. Finally, I drafted my solution set. Today, I will continue the application lifecycle management (ALM) process and explain to you how I’ll start developing my example…