Demo Code is here: github.com/Arize-ai/tu...

Watch the webinar here! www.youtube.com/watch?v=n-O...

In our latest Evals Series webinar, we covered how to evaluate your evaluator.

AI development has two loops, Meta-evaluation lives in the inner loop.

We also walked through a live demo of this loop in practice, iteratively improving the judge and showing measurable gains at each step.

Check out this walkthrough on bringing observability and evals into LLM workflows, plus a Phoenix demo with helpful context for anyone building agents in TypeScript.

Watch the session below 👇

Spoke at @arize.bsky.social’s AI Builder Meetup a few weeks back & the talk is now live!

Covered the basics of observability + evals, and showed via a Mastra agent how to set up tracing, run evals, & start your iteration cycle.

Check it out here 🚀
www.youtube.com/watch?v=qQGQ...

Learn to prompt better

🚀 Phoenix now supports MCP (Model Context Protocol)

This lets tools like Claude query prompts, datasets, and experiment results directly from a Phoenix instance (cloud or self-hosted).

Check out our docs to learn more about how to spin it up 👇

Here’s how it works:
www.youtube.com/watch?v=mHeZ...

🤖 Building agents, but not sure how to measure their performance?

Our newest blog post on @hf.co has you covered!

This post shows you how to use @arize-phoenix.bsky.social to trace and evaluate your smolagents.

Credit to @srichavali.bsky.social and @aymeric-roucher.bsky.social