# How do you evaluate LLMs?

**URL:** <https://discuss.huggingface.co/t/how-do-you-evaluate-llms/173768>\
**Category:** Research\
**Created:** [February 25, 2026, 7:10pm UTC](https://discuss.huggingface.co/t/how-do-you-evaluate-llms/173768 "2026-02-25T19:10:36Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![bibikong](https://avatars.discourse-cdn.com/v4/letter/b/c67d28/32.png) [@bibikong](https://discuss.huggingface.co/u/bibikong)\
**Post date:** [February 25, 2026, 7:10pm UTC](https://discuss.huggingface.co/t/how-do-you-evaluate-llms/173768/1 "2026-02-25T19:10:36Z")

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Hi, I’m curious how people here actually choose models in practice.

We’re a small research team at the University of Michigan studying real-world LLM evaluation workflows for our capstone project.

We’re trying to understand what actually happens when you:

```
•	Decide which model to ship

•	Balance cost, latency, output quality, and memory

•	Deal with benchmarks that don’t match production

•	Handle conflicting signals (metrics vs gut feeling)

•	Figure out what ultimately drives the final decision

```

If you’ve compared multiple LLM models in a real project (product, development, research, or serious build), we’d really value your input.

Short, anonymous survey (~5–8 minutes):

[https://forms.gle/xEGuDCZ3UBmitGCz6](https://forms.gle/xEGuDCZ3UBmitGCz6)