LangChain has published a guide to pairwise evaluation with LangSmith. The post explains when teams may want to compare two outputs directly rather than scoring each one independently.
Pairwise evaluation can be useful when product quality depends on relative preference, such as choosing between prompt variants, model versions, or retrieval strategies. It also mirrors how many human reviewers naturally judge model outputs.
The guide fits a larger trend: LLM application development is becoming evaluation-heavy, with teams needing repeatable methods to decide whether changes actually improve user-facing behavior.