Arize Phoenix: LLM and Agent Observability with - AI Build & Learn #11

​Welcome to AI Build & Learn a weekly AI engineering stream where we pick a new topic and learn by building together.

​This event is about observability and evaluation for LLM and RAG applications with Arize Phoenix, an open-source AI observability platform for tracing, evaluating, and debugging LLM apps.

​We'll explore Phoenix tracing for LLM and agent workflows, running evals on captured spans (including Ragas metrics from last event), and how to use Phoenix to debug retrieval and generation issues in real applications.

​Some things to look up to get started:

• ​Arize Phoenix GitHub: https://github.com/Arize-ai/phoenix

• ​Phoenix docs: https://docs.arize.com/phoenix

​Reources

• ​GitHub: https://github.com/sagecodes/ai-build-and-learn

• ​Events Calendar: https://luma.com/ai-builders-and-learners

• ​Slack (Discuss during the week): Flyte Slack Group

• ​Hosted by Sage Elliott: https://www.linkedin.com/in/sageelliott/

​In this stream

• ​Intro to topic

• ​Community Discussion

• ​Practical examples

​Community challenge (optional)

​Try spending 30–90 minutes during the week learning or building something related to the topic, then share what you’re working on in Slack.

​Note on Flyte / Union

​You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.

• ​Union: https://www.union.ai/

• ​Flyte: https://flyte.org/

​Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).