Error Analysis to Evaluate LLM Applications with Langfuse (open source)

Description

To improve your LLM app, you must understand how it fails. Aggregate metrics won’t tell you if your system retrieves the wrong documents or if the model’s tone alienates users. Error analysis provides this crucial context. This guide describes a four-step process to identify, categorize, and quantify your application’s unique failure modes. The result is a specific evaluation framework that is far more useful than generic metrics: 00:00 Introduction 01:48 Gather a diverse dataset of traces 03:51 Open code to surface failure patterns 06:34 Structure failure modes 07:42 Label and quantify [Synthetic Dataset Generation](https://langfuse.com/guides/cookbook/example_synthetic_datasets) [Exporting Comments from Langfuse:] (https://colab.research.google.com/drive/1ErETZNWHyOjkG262bZHh-j3Vo9z83qUj) The framework in this guide is adapted from Hamel Husain’s Eval FAQ.