Exploring Arize Phoenix for LLM-Based Sports Analytics Insights

·Jun 06, 2025 07:36 AM

Hi Arize Team, We are currently exploring Arize Phoenix for evaluating LLM-based workflows and agentic AI systems, particularly in the context of our AI-powered sports analytics platform focused on video-based insights. To better understand and experiment with Phoenix’s capabilities, I wanted to ask if there are any publicly available: Sample projects or notebooks Example workflows or evaluation pipelines GitHub repositories or tutorials This would help us get up to speed quickly and explore how Phoenix can be applied to trace, evaluate, and iterate on our LLM components effectively. Looking forward to any guidance or references you can share.

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RunLLM
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Yes, there are several public resources for getting started with Phoenix. You can find a curated list of example notebooks and tutorials—including tracing, evaluation, RAG analysis, and agent experimentation—on the <https://github.com/Arize-ai/phoenix/blob/main/docs/notebooks.md|Phoenix Notebooks page>. These link directly to runnable Colab notebooks, such as <https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/llm_ops_overview.ipynb|LLM Ops Overview>, <https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/evals/evaluate_rag.ipynb|Evaluating a RAG Pipeline>, and <https://colab.research.google.com/github/Arize-ai/phoenix/blob/main/tutorials/experiments/agents-cookbook.ipynb|Experimenting with Agents>. For full-stack application examples and integration guides (Python, JavaScript, DSPy, etc.), see the <https://github.com/Arize-ai/openinference/tree/main/python/examples|OpenInference examples> and the <https://docs.arize.com/phoenix|Phoenix documentation>. These resources cover tracing, evaluation pipelines, and dataset/experiment management, which should help you quickly prototype and iterate on your LLM workflows. Would you like more detail on a specific type of workflow or evaluation pipeline? Sources: - <https://github.com/Arize-ai/phoenix/blob/main/docs/notebooks.md|Phoenix Notebooks> - <https://docs.arize.com/phoenix|Phoenix Documentation>
💡 Hint: Mention RunLLM in the thread for followups.

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