Part 2 of a series on testing AI systems in production In Part 1, we explored why testing AI systems is fundamentally different from traditional software. We talked about non-determinism, prompt sensitivity, and why unit tests aren’t enough. Now let’s move from theory to practice. How do you actually build a system to test AI reliably? This post walks through a practical approach to building an AI evaluation pipeline —from dataset creation to CI/CD integration. What is an AI Evaluation Pipeline? At a high level, an evaluation pipeline looks like this: Dataset → System → Evaluation → Metrics → Analysis Enter fullscreen mode Exit fullscreen mode More concretely: You define a dataset of test cases Run them through your AI system Evaluate outputs using defined metrics Store and analyze results over time This becomes your source of truth for system quality . Step 1: Build a High-Quality Evaluation Dataset Your evaluation pipeline is only as good as your dataset.…