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From Guesswork to Ground Truth: Using Evaluation to Unlock Smaller, Cheaper LLMs
At Evrim, we found that significantly smaller language models could match (and in some cases outperform) much larger models on translation tasks, while delivering substantial cost and latency savings. In this post, we cover how we approached this evaluation problem in practice, introduce LLM-as-a-Judge as a scalable tool, and share our takeaways that enabled more confident model selection decisions.