{"id":12471,"date":"2025-01-03T13:18:48","date_gmt":"2025-01-03T21:18:48","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=12471"},"modified":"2026-02-02T19:13:17","modified_gmt":"2026-02-02T19:13:17","slug":"llm-evaluation-metrics-every-developer-should-know","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/llm-evaluation-metrics-every-developer-should-know\/","title":{"rendered":"LLM Evaluation Metrics Every Developer Should Know"},"content":{"rendered":"\n
\"title<\/figure>\n\n\n\n


\nWhen you build an app or system on top of an LLM, you need a way to understand the quality and consistency of the model\u2019s responses. The LLM\u2019s tone, accuracy, relevance, and other characteristics can have a major impact on user experience and adoption. Recording a set of LLM responses, spot checking, and manually annotating them gives you a great starting point to optimize how your system interacts with the LLM. But when it comes to larger datasets and more complex systems, it\u2019s important to automate scoring as a way to better understand how your application is performing on the whole.<\/p>\n\n\n\n

LLM evaluation<\/a> metrics let you establish a numeric baseline for certain aspects of your LLM responses, and try to improve that number by either changing your prompts, building a better RAG system, or upgrading to the latest and greatest model. This is the workflow most teams are following during the LLM App development lifecycle. But what are some examples of LLM evaluation metrics? How do you calculate them, and which metrics work best in different scenarios?<\/p>\n\n\n\n

The answer to that last question depends largely on your use case. If you are building an LLM app for summarization, then your criteria for success and therefore evaluation metrics are going to look different from someone who is utilizing LLMs for machine translation.<\/p>\n\n\n\n

Types of LLM Evaluation Metrics<\/h2>\n\n\n\n

In this blog we\u2019ll walk through some of the most popular evaluation metrics for LLM powered chatbots, summarization AI agents<\/a>, machine translation systems, and more! Before we dive into some actual evaluation scores, let\u2019s look at some of the different workflows teams are using for LLM evaluation<\/a>.<\/p>\n\n\n\n

As mentioned above, manual annotation, or human-in-the-loop<\/a> feedback, is the most intuitive way to score LLM responses. While manual annotation has its benefits of handling nuance and subjectivity, it very quickly becomes unfeasible when you have hundreds or thousands of samples to score. To move faster, developers are setting up automated evaluations to quickly run an evaluation on a dataset and see if the scores are better or worse than the previous run.<\/p>\n\n\n\n

These automated LLM evaluation metrics typically break down into two categories:<\/p>\n\n\n\n