This blog post discusses how to troubleshoot Large Language Model (LLM) summarization tasks using Arize-Phoenix, an open-source library offering ML observability in a notebook for surfacing problems and fine-tuning generative LLM models. The tutorial guides the reader through analyzing prompt-response pairs, computing ROUGE-L scores, and leveraging Phoenix to find the root cause of performance issues in an LLM summarization model. By following these steps, the reader can identify specific areas where the LLM is struggling and take corrective actions to improve its performance, such as modifying prompt templates or excluding articles from certain languages. The tutorial concludes by highlighting the importance of monitoring LLM performance and identifying specific areas of weakness to improve overall model performance.