Evaluating foundation models requires more than lab benchmarks. Human review, ROUGE, BLEU, and BERTScore each offer partial views of performance. Humans assess tone and policy compliance better than automated tools, but at higher cost.

Business outcomes must guide evaluation. Metrics like ROUGE help with summarization, BLEU for translation, and BERTScore for semantic similarity. However, strong metric scores don’t guarantee real-world success if speed, safety, or user experience suffer.

For RAG and agent systems, assess retrieval quality, grounding, and hallucination rate. Task completion, tool usage accuracy, and compliance matter more than base model scores. Amazon Bedrock Evaluations offers managed workflows to standardize these comparisons across models and prompts.

Next, teams should align model evaluation with productivity gains, user engagement, and task success rates. Focusing only on text similarity metrics risks deploying models that fail in production or miss business goals.