Auto-create reliable LLM evals

Learn how to build reliable LLM-based evaluations using only about twenty human annotations, achieving scalable, human‑aligned assessments that outperform existing benchmarks.

Overview

Evaluating AI apps is often only possible by humans reviewing outputs or using LLMs to evaluate them. The former is laborious, slow, and expensive. The latter often falls short of correctly evaluating the outputs.

We’ve developed a method to create reliable LLM-based evals with as few as 20 manual annotations. Allowing you to automagically turn “vibe checks” into scalable and reliable evaluations aligned with human judgment.

We’re also releasing preliminary results on JudgeBench on which our automatically created LLM eval using gpt-3.5-turbo beats gpt-4o.

Links

https://docs.parea.ai/blog/self-improving-llm-evals

Tech stack