Why Quality Annotation Improves LLM Training

In the era of Generative AI, the bottleneck has shifted from model architecture to data quality. Large Language Models (LLMs) are prone to hallucinations and reasoning errors when trained on noisy, unverified datasets.

At DeepAnnotation, we focus on 'Ground Truth Engineering'. Unlike basic crowdsourcing, our workflows involve domain experts who verify reasoning chains (CoT) and factual accuracy.

Furthermore, clean, human-verified data significantly accelerates model convergence, saving substantial GPU compute costs.