Developer warns against building LLM eval frameworks from scratch after costly lesson

A software developer spent a weekend building a 200-line Python framework to evaluate large language model outputs, only to find it unmanageable six months later. The system broke down due to inconsistent judge model outputs, stale test datasets, and a CI pipeline that failed whenever a vendor updated their model. Reflecting on the experience, the developer argues that teams should only build eval components that are domain-specific and improve over time, such as rubrics, curated datasets, and gating rules. Generic infrastructure like retry loops, parallel runners, and output parsers is rebuilt repeatedly across companies with little added value. The key lesson is to invest in what encodes your domain knowledge and reuse or buy everything else.