Automate AI evaluations with Microsoft Foundry and GitHub Actions: Review

Running that `eval.py` script locally and seeing it spit out coherence scores was the point. I’ve been messing with one-off Python scripts to evaluate RAG outputs, and it’s a mess of hardcoded prompts and manual CSV checks. This module puts a name to the pattern: GenAIOps. Fine, I'll bite.

The content itself is a fast read. It basically argues: stop running evals in notebooks, version your prompts in Git, and trigger evaluations with a CI/CD pipeline. No arguments there. The key insight for me was seeing how Foundry's evaluators (`coherence`, `groundedness`) are just functions you call in a standard Python script, which then gets orchestrated by GitHub Actions. It’s not magic; it’s just good software engineering applied to LLMs.

The exercise is a fill-in-the-blanks job. You're not writing the logic, just connecting the dots. But it does force you to look at the structure of the `eval.py` script and the `ci.yml` file. I spent more time reading the sample code in the repo it points to than the module text itself. That’s where the value is. It’s a template. I can take this `ci.yml`, gut it, and adapt it for my own RAG project using my own data. It saves me a day or two of architecting from scratch. So, yeah, it's a glorified product tutorial, but it’s a useful one.