Offline evaluations assess LLM pipeline quality before deployment using curated datasets with known expected outputs. A golden dataset contains input + ideal output pairs representing critical cases. Automated metrics (exact match, ROUGE, semantic similarity, LLM-as-judge) score each sample. CI gates block deployment if eval scores drop. Offline evals catch regressions from prompt changes, model upgrades, or retrieval changes before they reach users.
Each stage in order — click any step to read what it does.
Offline eval pipeline with CI gate.
The trade-offs worth knowing before you build this.
Any single metric can be gamed or may not reflect real quality. A well-curated golden dataset with diverse, representative examples is the foundation. Metrics are only as good as the dataset they run against.
Prompt changes often feel like improvements but subtly degrade specific cases. Without an eval gate, these regressions reach production silently. A CI eval gate catches them on every PR.
Low-scoring samples often cluster around specific input types. Are all chemistry questions failing? Are multi-hop reasoning questions wrong? These patterns reveal systematic issues to fix.
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