Automated AI Evaluation with AI Foundry and GitHub Actions

Deploying AI agents and LLM applications requires continuous validation to ensure quality, safety, and accuracy. Manual testing cannot scale as your models, system prompts, and data sources evolve. This text-only course guides you through the process of automating your AI evaluation workflows. You will learn how to integrate AI Foundry evaluators with GitHub Actions pipelines to run automated quality checks on every code change, ensuring your AI agents perform reliably. What you'll learn: - Understand the core principles of AI evaluation metrics, including groundedness, relevance, and coherence - Configure AI Foundry evaluators to systematically assess the outputs of your language models - Build GitHub Actions workflows to trigger automated evaluations on code commits and pull requests - Analyze evaluation results to detect performance regressions and prompt drift - Apply modern LLMOps practices to continuously improve and monitor your AI agents You will start with foundational AI evaluation concepts before moving on to configuring evaluators and writing GitHub Actions workflow files. Through step-by-step written explanations and practical configuration examples, you will learn how to establish a robust CI/CD pipeline for AI quality assurance. This course is designed for software developers, aspiring LLMOps engineers, and AI enthusiasts who want to automate their quality assurance processes. No prior experience with AI evaluation tools is required, though a basic understanding of Git is helpful. Start reading today to build reliable, automated testing pipelines for your AI applications.