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Is it worth developing your own large language model (LLM), or is it strategically smarter to use existing models and buy AI software? LLMs have given artificial intelligence its breakthrough: applications like ChatGPT proved in a very short time that they have the potential to multiply company productivity. For decision-makers at large and mid-sized companies, the question has long since stopped being whether and is now only how best to integrate generative AI into their value chain.

In our dynamic world of custom software development, quality assurance (QA) plays a decisive role. First, to make sure your software solution runs smoothly, and second, to meet the requirements and expectations of your users. For my team and me, quality assurance goes far beyond simply detecting errors and extends to ensuring quality, reliability and usability.

In a world increasingly shaped by technological progress, artificial intelligence (AI) opens up fascinating possibilities as well as potentially unsettling challenges, especially in the context of processing personal data. The advancing integration of AI algorithms into various areas of our lives promises gains in efficiency, personalised services and innovative approaches to problems. At the same time, though, the growing volume of collected data and the complexity of AI systems raise legitimate questions about data protection, privacy and ethical responsibility.

The world of AI development technologies has changed a great deal in recent years. Large language models (LLMs), and more recently large multimodal models, along with numerous new tools, open up many possibilities for individual developers, entire teams and companies.

Church services, administration, community. Since the COVID-19 pandemic, digitalization in the church has been happening at breakneck speed. In an immense pressure situation, digital solutions were created in many places within a very short time that had still seemed far off two years earlier.