Evaluation Methods & Human Assessment

When benchmarks aren't enough - and for complex, open-ended tasks, they rarely are - evaluation relies on human judgement and more sophisticated methods. Human evaluation involves having people rate model outputs on criteria like helpfulness, accuracy, coherence and safety. This is the gold standard for quality assessment because humans can evaluate nuances that automated metrics miss - but it's slow, expensive and subjective, and two evaluators may disagree about whether a response is good. To address this, the field has developed various approaches: model-as-judge (using one AI model to evaluate another's outputs), Elo rating systems (comparing models head-to-head like chess rankings), arena-style evaluations (where users vote on which of two anonymous models gave the better response) and task-specific rubrics that standardise human judgement. Each approach has trade-offs between cost, speed, consistency and the richness of feedback it provides. For businesses, the practical implication is that you should invest in evaluation before investing in AI itself. Define what "good" looks like for your specific use case, create representative test cases and measure systematically. The organisations that succeed with AI are the ones with rigorous evaluation practices, not just the ones using the fanciest models.