A Practical Guide to Evaluating LLM Performance
Evaluating LLMs is notoriously difficult. This guide covers the frameworks, metrics, and tools that actually work.
By Lisa Zhang
Evaluating LLM performance is one of the hardest problems in applied AI. Here's a practical framework.
Human Evaluation
Despite advances in automated evaluation, human judgment remains the gold standard for many tasks. Structure your human eval with clear rubrics and multiple annotators.
Automated Metrics
BLEU, ROUGE, and similar metrics are insufficient for LLMs. Use LLM-as-judge approaches with careful prompt engineering, but validate against human judgments.
Task-Specific Benchmarks
Build your own evaluation suite that reflects your actual use case. Generic benchmarks like MMLU are useful for model selection but don't predict production performance.
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