In recent years, the development of artificial intelligence (AI) systems based on Large Language Models (LLMs) has transformed how we interact with textual data, as well as the possibilities for generating, understanding, and analyzing human language on a large scale. In the fields of Digital Humanities and Natural Language Processing (NLP), these models have demonstrated unprecedented performance in tasks such as text generation, machine translation, summarization, semantic classification, and automatic question answering. However, this success has been accompanied by a fundamental challenge that still lacks a standardized and systematic solution: the quality of the data used to train these systems.