AI

A lot of teams building on top of LLMs ship a prompt, look at a handful of outputs, decide it "seems good," and move on. That works right up until a model update, a prompt tweak, or an edge case quietly breaks something nobody's testing for. Evals are the fix — the same instinct that gives you unit tests and regression tests for regular code, applied to something that doesn't produce the same output twice.

If you've tried running a large language model on your own hardware, you've probably run into the same wall everyone does: the model is huge, your GPU memory is not, and downloading a "7B" or "70B" model suddenly feels like a math problem. Quantization is the main trick the community uses to close that gap.