Parea

Built for engineers and product teams shipping LLM-powered products, Parea automates the creation of domain-specific evaluation functions by bootstrapping them from a small number of human annotations, enabling teams to continuously measure AI output quality without scaling their manual review workload proportionally with usage.

The platform's core innovation is its automated eval creation workflow, which generates domain-specific evaluation functions from as few as 20 sample human annotations.

Teams label a representative sample of AI outputs as good or bad according to their quality criteria, and Parea uses this signal to construct an evaluation function that generalizes to new outputs applying the same judgment criteria automatically at scale.

This dramatically lowers the cost of maintaining rigorous quality standards as LLM applications move from prototype to production with growing traffic volumes.

Parea provides a comprehensive suite of evaluation metrics out of the box, including Levenshtein distance, LLM-as-grader approaches, answer relevancy scoring, self-consistency checks, and LM-versus-LM factuality evaluation.

These pre-built metrics cover the most common quality dimensions for LLM applications accuracy, relevance, faithfulness, and consistency enabling teams to begin measuring quality immediately without defining custom evaluation frameworks from scratch for standard assessment needs.

The platform's prompt playground and experimentation environment allows teams to run side-by-side comparisons of different prompt versions across test case datasets with evaluation metrics applied automatically.