Machine Learning Mastery has published a tutorial on building a custom LLM evaluation harness in Python from scratch. The design centers on four modular components, which the article says give developers flexibility when testing language models.
Because the harness is custom, developers can adapt it to their own evaluation needs rather than relying on off-the-shelf tools. The tutorial walks through implementing the components in Python, with the modular structure intended to make the system easier to extend and maintain.
Since there is only one source, there are no conflicting views to reconcile. The article's main claim is that a from-scratch, modular harness is a practical way to evaluate LLMs.