microsoft/genaiops-promptflow-template

GenAIOps with Prompt Flow is a "GenAIOps template and guidance" to help you build LLM-infused apps using Prompt Flow. It offers a range of features including Centralized Code Hosting, Lifecycle Management, Variant and Hyperparameter Experimentation, A/B Deployment, reporting for all runs and experiments and so on.

What it solves

This project provides a standardized template and guidance for implementing LLMOps (Large Language Model Operations) using Prompt flow. It addresses the difficulty of managing the end-to-end lifecycle of LLM-infused applications—from local experimentation and prompt engineering to production deployment and monitoring—especially when handling multiple concurrent flows with unique requirements.

How it works

The template establishes a structured framework for developing, evaluating, and deploying Prompt flow workflows. It supports three types of flows: YAML-based (DAG), Python function-based, and Python class-based. It automates the transition between environments by supporting local execution, Azure AI Studio, and Azure Machine Learning. The system integrates with CI/CD tools like GitHub Actions, Azure DevOps, and Jenkins to orchestrate the outer-loop deployment process, while providing tools for inner-loop experimentation, including variant and hyperparameter testing and detailed metric reporting.

Who it’s for

It is designed for engineers, data scientists, and developers building LLM-infused applications who need a disciplined, scalable approach to prompt engineering and operationalization.

Highlights

  • Multi-Flow Management: Centralized hosting for multiple flows in a single repository with independent lifecycles.
  • C’D Integration: Built-in support for GitHub, Azure DevOps, and Jenkins orchestration.
  • Flexible Deployment: Deploy flows to Kubernetes, Azure Web Apps, and AzureML/AI Studio managed compute, including A/B deployment capabilities.
  • Experimentation Tools: Support for variant and hyperparameter experimentation with comprehensive HTML/CSV reporting.
  • DataOps Integration: Separate data pipelines to ensure disciplined dataset preparation for experimentation and evaluation.
  • Hybrid Execution: Ability to run flows locally or on Azure cloud based on configuration.