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AI Lab · by Mariam Said · 2026-10-17

How are you evaluating LLM outputs without a giant labeling budget?

We use a mix of golden sets, LLM-as-judge and spot checks by humans. Judge models seem biased toward long answers. What is working for you?

25 sparks · 13 comments

Comments

Miguel Santos · 10 likes

We tried LLM evals last quarter, worked great for small stuff, got messy at scale

Camila Soto · 4 likes

send help

Jasmine Cruz · 8 likes

cost vs benefit matters here, we ran numbers on LLM evals and the payoff came later than expected

Pixel Orbit · 0 likes

send help

Haruto Sato · 7 likes

As someone who does this for a living, the biggest cost with LLM evals is the stuff nobody puts in the demo!

Leila Karimi · 3 likes

wait until you see the invoice

Léa Bernard · 6 likes

Is anyone else tired of the hype around LLM evals, feels like every other post is about it

Silver Marauder · 3 likes

The beginner friendly path for LLM evals is way better than a year ago, that part is underrated

Sanne de Vries · 1 likes

Would love to see actual data on LLM evals instead of vibes. Anyone have links

Siti Lestari · 0 likes

My manager read one article on LLM evals and now it's our whole roadmap lol

Cozy Noodle · 8 likes

yeah thats what I meant

Diego Quispe · 0 likes

LLM evaluation sounds simple until you try it with real constraints.

Mia Bauer · 8 likes

this. so much this