The best approach to compare LLM outputs

Once large language models (LLMs) are in production, evaluating output quality becomes an operational priority, focusing on stability and improvement rather than subjective assessments. As LLM systems undergo constant changes, such as prompt iterations and model swaps, traditional manual reviews and ad-hoc prompting prove insufficient at scale due to their inconsistency and narrow scope. Effective evaluation requires a combination of deterministic metrics, like regex matching, and model-based metrics, which assess subjective qualities like coherence and relevance. Arize's approach treats evaluation as a continuous operational loop, integrating pre-built and custom evaluators to assess key dimensions like hallucination and relevance, while providing actionable explanations for diagnostics. Portkey's AI Gateway facilitates the orchestration of evaluations by routing LLM traffic through consistent APIs, allowing robust comparisons across models and configurations in both testing and production environments. This comprehensive evaluation framework ensures that insights are actionable and aligned with operational goals, enabling teams to iterate confidently and efficiently.