This paper introduces CAT-GPT, an innovative Computer-Assisted Translation (CAT) tool designed to address context-awareness and terminological consistency challenges often encountered in standard CAT workflows. Grounded in Skopos theory (Vermeer, 2014) and powered by a Large Language Model (LLM) backend, CAT-GPT integrates context-sensitive segmentation, automatically generated and adjustable translation instructions, and an advanced machine translation component. Comparative observations with a widely used CAT tool (e.g., Trados Studio) suggest that CAT-GPT reduces post-editing effort and improves text-level coherence, especially in specialized or domain-specific scenarios.