Data labeling is often treated as logistics, but Eugene Yan's guide to writing labeling and annotation guidelines treats the instructions themselves as a core part of producing good training data. The post focuses on the practical craft of documenting what a label means, so that annotators can work accurately and at reasonable speed.
Clear instructions reduce the chance that different annotators will interpret the same case in opposite ways. When guidelines are coherent, teams can get both better precision and faster throughput from the labeling process. That makes them a direct lever on the quality of downstream models.
Because this article is based on a single source, there are no differing perspectives to compare. The value is in the framing: labeling guidelines are not a formality, but a mechanism for controlling data quality in AI pipelines.