Developer Builds Custom LLM Eval System to Cut Through Model Launch Hype

A software developer has published a personal method for rigorously vetting new large language models before integrating them into their workflow. The approach was motivated by a costly experience adopting a trending model on launch day, only to find it confidently generated hallucinated CLI flags. Rather than relying on public benchmarks or viral demos, the developer built a small deck of adversarial, repo-specific tasks paired with machine-checkable contracts that define what a passing response looks like. A lightweight, dependency-free Node.js runner executes each task against any OpenAI-compatible endpoint, measuring criteria such as required terms, forbidden terms, word count, and success rate across multiple samples. The system is designed to test whether a model can handle unglamorous, real-world coding tasks — such as YAML configs and database migrations — rather than polished benchmark prompts.