Whether text generation, translation or smart chatbots, Large Language Models (LLMs) are currently one of the most exciting AI innovations and are changing the face of corporate communications. They understand, process and generate language and already perform a wide range of tasks and processes. However, in order to deliver not just generic, but company-specific results, the systems must be instructed and trained. We explain the ways in which LLM output can be customised to your company's own style and the important role your language data from translation memories and terminology databases plays in this.

DeepL or ChatGPT promise fast translations at the touch of a button – but what if the result is disappointing? Many companies are facing precisely this challenge: although the translation by artificial intelligence sounds fluent, a closer look reveals errors in the technical terminology and inaccuracies in the content. We explain how to deal with faulty AI translations and when professional translation processes are essential.

Professional translation is in a state of flux. Machine translation and AI are changing the requirements. For companies that rely on professional translation, this poses the question: what role do translators play in this new world? The answer may come as a surprise: their importance is growing – only the tasks they do are changing fundamentally.

Many companies are already relying on machine or AI-generated translation solutions to speed up processes and reduce costs. In practice, however, the savings are often not as large as expected. Just because AI is being used does not mean that optimal results or significant cost reductions are guaranteed. Realising the full potential requires a well thought-out strategy, targeted adjustments at various points in the translation process and human interventions in exactly the right places. With our five-step plan, we show you how you can sustainably and noticeably reduce your translation costs with AI support, without compromising on quality.

The rapid change brought about by the integration of artificial intelligence is affecting almost all industries and is also revolutionising the translation industry. The industry is used to drastic changes, as the breakthrough of neural machine translation (MT) caused disruption just a few years ago. Since then, MTPE – the combination of MT and post-editing (PE) – has established itself as a method for bringing machine translations to a professional level. The emergence of Generative Artificial Intelligence (GenAI) in the form of Large Language Models (LLMs) such as GPT, Mistral and Llama again begs the question of whether and how translation processes will continue to evolve and change.