Linigu Blog
Professional articles on translation, CAT tools, and language technology
Bienvenido al Blog de Linigu
El Blog de Linigu es un espacio de conocimiento para traductores profesionales y expertos en localización. Aquí publicamos artículos sobre flujos de trabajo de traducción, herramientas CAT como SDL Trados Studio, formatos de archivos y gestión terminológica.
linigu.cloud fue creado para resolver problemas reales del sector de la traducción. En el blog explicamos cómo trabajar con archivos SDL y cómo utilizar el Linigu SDL Studio Converter para visualizar archivos complejos en Word o Excel de forma rápida y sencilla.
Nuestro objetivo es ayudarte a optimizar tu trabajo, mejorar la calidad lingüística y comprender mejor las tecnologías de traducción modernas.
Proofreading a human translation and post-editing a machine translation may seem similar, but they require different skills, mindsets, and risk awareness. This article explains why.
Using large language models like ChatGPT or Claude to translate entire PDF documents might seem convenient and cost-effective, but this approach introduces serious consistency problems that professional translators and organizations must understand. When the same document is translated multiple times by AI, terminology choices, tone, and sentence structures vary unpredictably—creating quality issues that undermine translation reliability and brand consistency.
Integrating DeepL machine translation with SDL Trados Studio represents one of the most powerful combinations in modern translation technology. DeepL's superior neural machine translation quality paired with Trados's robust translation memory and terminology management creates workflows that dramatically accelerate translation. However, this integration introduces specific challenges, particularly around terminology consistency and quality control, that translators and project managers must unde
Machine translation has evolved from a curiosity to a practical tool used across industries worldwide. Modern neural machine translation systems produce remarkably fluent output, yet they also exhibit predictable weaknesses that limit their application. Understanding where MT excels and where it struggles enables organizations to deploy it effectively while recognizing when human expertise remains essential.