The proper use of LLMs

Dear Readers,

Sometimes it's not the shiny models themselves that make the difference, but the way we work with them. Anyone who believes that productivity depends solely on the right “magic formula” in the prompt is underestimating the real revolution: clear processes, precise specifications, and smart use of tools. This is precisely where it is decided whether AI remains just a nice toy—or whether it truly becomes a reliable co-pilot.

In this issue, we look at how LLMs can best be used in practice: from clever prompt design to RAG and agents to systematic quality assurance. We also take a look at their limitations, show specific areas of application in everyday life, business, and medicine, and present methods for turning mere text flow into verifiable results. If you want to understand how AI not only makes things faster but also better, read on.

All the best,

Anyone working with large language models today quickly realizes that the real magic lies not in the model itself, but in the way we work with it. Studies from the fields of economics and software development estimate productivity gains of between 14% and over 40%, depending on the task; in controlled experiments with code assistants, accelerations of around 55% were even measured. The decisive factor here is not so much a mysterious “prompt formula” as a well-thought-out work process: clear goals, precise specifications, suitable tools, and systematic quality assurance.

This text provides a practical yet robust overview: How can tasks be structured so that LLMs provide reliable assistance? Which prompt techniques actually work? When do you need tools such as retrieval-augmented generation (RAG), function calls, and agents—and how can you use them effectively in everyday life, business, and medicine? The key question we answer in the course of this article is: How can LLMs be used not only to work faster, but above all to improve quality—in a reproducible, verifiable, and responsible manner?