Notes on LLM Evaluation

Building an effective evaluation pipeline is a critical, data-centric part of developing any LLM-based application. The process begins with collecting and annotating data, which involves both error analysis of existing outputs and defining success by creating ideal reference answers. A key technique is writing detailed rubrics for each example to establish clear, objective criteria for what constitutes a good response. These steps create a versioned evaluation dataset that enables a repeatable process for measuring performance and iterating on the application. The guide uses an AI-powered IT helpdesk assistant as a running example to illustrate these concepts in practice.