Error Analysis to Evaluate LLM Applications

Error analysis is essential for improving LLM applications by identifying and categorizing failure modes, as aggregate metrics often fail to provide the necessary detail about system performance. The guide, adapted from Hamel Husain's Eval FAQ, outlines a four-step process involving data gathering, open coding, structuring failure modes, and labeling and quantifying errors using Langfuse. By using a demo chatbot as an example, the process demonstrates how to collect a diverse dataset, annotate traces with failure patterns, and organize these into a coherent taxonomy. This method highlights specific issues such as context retrieval problems, which are identified as the most common failure mode in the example, and emphasizes the importance of recurring analysis as applications evolve. The insights gained from this process guide targeted improvements and serve as a foundation for developing automated evaluators to scale the analysis.

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