Operationalize your Prompt Engineering Skills with Azure Prompt Flow
In today’s AI-driven world, prompt engineering is a game-changing skill for developers and professionals alike. With Azure Prompt Flow, you can harness the power of open-source LLMs to solve real-world operational challenges! This article guides you through using Azure’s robust tools to build, deploy, and refine your own LLM apps—from chatbots to data extraction tools and beyond. Whether you're just starting or looking to sharpen your AI expertise, this guide has everything you need to unlock new possibilities with prompt engineering. Dive in and take your tech journey to the next level!Journey Series for Generative AI Application Architecture - Model references and evaluation models
In the previous content, we integrated the entire SLMOps process through Microsoft Olive. The development team can configure everything from data, fine-tuning, format conversion, deployment, etc. through Olive.config. In this article, I hope to talk about model reference and evaluation.Journey Series for Generative AI Application Architecture - Foundation
At Build last year, Microsoft CTO Kevin Scott proposed Copilot Stack to provide problem-solving ideas for Generative AI applications. Based on the Coplit Stack, community have developed many frameworks in the past year, such as Semantic Kernel, AutoGen, and LangChain. These frameworks are more biased toward front-end applications, and enterprises need a better engineering solution. This series hopes to give you some ideas based on Microsoft Cloud and related frameworks and tools.Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow: Step-by-Step Guide
In this tutorial, you will learn how to fine-tune the Phi-3 model and integrate it with Prompt Flow. By leveraging Azure Machine Learning, and Prompt flow you will establish a workflow for deploying and utilizing custom AI models.
