Missed the news from Arize Observe 2025? Phoenix Cloud just got Spaces & Access Management!

✨ Create tailored Spaces
🔑 Manage user permissions
👥 Easy team collaboration

More than a feature, it’s Phoenix adapting to you.

Spin up a new Phoenix project & test it out!
@arize-phoenix.bsky.social

🆕 New in OpenInference: Python auto-instrumentation for the Google GenAI SDK!

Add GenAI tracing to your @arize-phoenix.bsky.social applications in just a few lines. Works great with Span Replay so you can debug, tweak, and explore agent behavior in prompt playground.

Check Notebook + docs below!👇

Learn to prompt better

Just dropped a tutorial on using the OpenAI Agents SDK + @arize-phoenix.bsky.social to go from building to evaluating agents.

✔️ Trace agent decisions at every step
✔️ Offline and Online Evals using LLM as a Judge

If you're building agents, measuring them is essential.

Full vid and cookbook below

We've added GPT-4.1 models to the @arize-phoenix.bsky.social Prompt Playground.

My go-to way to test out these new models: grab a failed trace from a previous run, pull it into playground, switch the model and see if 4.1 can succeed where 4o failed.

Early signs are promising!

LLM as a Judge allows models to evaluate outputs in a single prompt—but a good judging needs a good prompt

In my new tutorial, learn techniques on how to optimize your prompt so your judge can improve accuracy, cost, fairness, and robustness

better prompts ➡️ better evals

Think + Act — all within your prompt

In this tutorial, I apply ReAct principles to prompt LLMs to Reason + Act like humans. By specifying these steps, the LLM generates reasoning and interacts with tools for greater accuracy.

Full Video Tutorial: youtu.be/PB7hrp0mz54?...

How much LLM reasoning can you drive through your prompt itself?

I’ve been using Chain of Thought (CoT) prompting to help LLMs replicate logical step-by-step thinking.

For the next segment in my prompting series, I use @arize-phoenix.bsky.social to test the performance of various CoT methods

🎉 5000 Stars and Counting... 🎉

We're celebrating Phoenix reaching 5000 stars on GitHub! This milestone underscores the growing demand for robust, open-source tools that tackle the complexities of AI and LLM development

Check it out: github.com/Arize-ai/pho...

www.youtube.com/watch?v=bW5Z...

How much more data does an LLM app really need?

In my latest tutorial, I explore how few-shot prompting boosts accuracy without massive datasets or retraining—using @arize-phoenix.bsky.social prompts and experiments to break it down.

This kicks off my prompting series... more to come!

🧠 Phoenix now supports Anthropic Sonnet 3.7 & Thinking Budgets!

This makes Prompt Playground ideal for side-by-side reasoning tests: o3 vs. Anthropic vs. R1.

Plus, GPT-4.5 support keeps it up to date with the latest from OpenAI & Anthropic - test them all out in the playground! ⚡️

Some updates for Projects! Gain more flexibility and control with:

📌 Persistent column selection for consistent views
🔍 Filter data directly from tables with metadata and quick metadata filters
⏳ Set custom time ranges for traces & spans
🌳 Option to filter spans by root spans

Check out the demo👇

Prompt optimization is essential, and automating it with frameworks like DSPy gives you scalable and data-driven improvements.

There's also a tutorial linked in here where you can use Phoenix to compare the performance of different techniques. 👇

arize.com/blog/prompt-...