This article was originally published on AI Study Room . For the full version with working code examples and related articles, visit the original post. Model Evaluation: Benchmarks, Human Evaluation, LLM-as-Judge, and A/B Testing in Production Choosing the right model for your application is not about picking the most powerful one. It is about picking the model that delivers sufficient quality at acceptable cost and latency. Here is how to build a model evaluation pipeline that gives you data-driven answers. Why Systematic Evaluation Matters Model selection based on leaderboard scores or blog posts is unreliable. A model that scores highest on MMLU might perform poorly on your specific task. Your data distribution, prompt structure, and quality requirements are unique. Systematic evaluation removes guesswork. You define what "good" means for your application, measure candidate models against that definition, and pick the winner based on evidence rather than hype. Evaluation also catches regressions.…