Azure Prompt Flow Output Trace Not Displaying Expected Details
We are experiencing an issue with the Prompt Flow (UI) trace visualization in Azure AI Studio. When we run a prompt flow and go to the Outputs > Trace tab, we only see a single flow node with 0 Tokens and the total duration. However, we do not see the detailed breakdown of intermediate nodes or any expanded trace graph (as shown in the https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/flow-develop#understand-the-trace-view). Expected Behavior: Upon clicking on the flow node, the right pane should show the detailed flow overview, including duration and token usage across individual steps/nodes. A full trace graph of the execution should be rendered. Current Behavior: Only the top-level flow is visible. No token information or trace details are available for sub-nodes, even though the flow has multiple components. Could you please advise whether this is a known issue or if there's any configuration needed to enable the full trace view?LLM based development tools: PromptFlow vs LangChain vs Semantic Kernel
Globally, developers, data scientists, and engineers created new applications or advanced their existing applications to take advantage of LLMs. While building a Question-and-Answer chatbot is simple and you may not need advanced tools, in other more complex scenarios, the AI orchestrator come in and make the process easier. At the center of LLM applications is the AI orchestration layer that allows developers to build their own Copilot experiencesEvaluate Fine-tuned Phi-3 / 3.5 Models in Azure AI Studio Focusing on Microsoft's Responsible AI
Fine-tuning a model can sometimes lead to unintended or undesired responses. To ensure that the model remains safe and effective, it's important to evaluate the model's potential to generate harmful content and its ability to produce accurate, relevant, and coherent responses. In this tutorial, you will learn how to evaluate the safety and performance of a fine-tuned Phi-3 / Phi-3.5 model integrated with Prompt flow in Azure AI Studio. Before beginning the technical steps, it's essential to understand Microsoft's Responsible AI Principles, an ethical framework designed to guide the responsible development, deployment, and operation of AI systems. These principles guide the responsible design, development, and deployment of AI systems, ensuring that AI technologies are built in a way that is fair, transparent, and inclusive. These principles are the foundation for evaluating the safety of AI models.How to build a social media assistant with Prompty and PromptFlow
Large Language Models were trained by data from all over the internet with billions of parameters. To generate an output from LLMs, you need a prompt. For day-to-day questions for example generating a LinkedIn Post from a blog post, you may need additional instructions for your LLM, for example: the word count, tone of the message, the format and the call to action at the end. Using prompty, you can easily standardize your prompt and execute it into a single asset
