multivon-eval
by multivon-ai
Practical LLM evaluation with deterministic checks and LLM-as-judge
Overview
Provides practical LLM evaluation primitives for teams shipping to production, combining deterministic checks with LLM-as-judge comparisons. Supports dataset-driven tests, hallucination detection, and CI/CD integration so evaluations run as part of pipelines. Distinctive features include configurable deterministic evaluators alongside LLM-judged metrics and tooling for RAG and prompt-focused assessments.
Why It Matters
Best For
Teams building production LLM pipelines who need CI-friendly evaluation, hallucination detection, and reproducible metrics. To support robust, repeatable validation strategies, consider how the Mutual Verification Pattern can complement these evaluation primitives.
Use Cases
- Automate hallucination detection in RAG systems as part of CI
- Run deterministic and LLM-judged benchmarks on datasets before deployments
- Gate releases with reproducible evaluation metrics for prompt or model changes