Blog

Language models such as GPT, BERT, and LLaMA have impressive language comprehension skills. They can summarize, formulate, or translate texts—and even show a touch of style awareness. But one crucial disadvantage remains: the knowledge of these models ends at the point of training. So they still know who discovered America, but not who just won the soccer championship or what new AI developments are currently making the rounds. Company-specific information is also left out. For example, a standard model cannot answer questions about the latest BCS releases. This is where RAG (Retrieval-Augmented Generation) comes into play. RAG gives the language model access to current or subject-specific information. This means that AI can not only draw on its pre-trained knowledge, but also on current data that is specifically relevant to the task at hand.

In today's world of artificial intelligence (AI), transformer models such as BERT, GPT, and LaMDA are ubiquitous. These models are revolutionizing natural language processing (NLP) and enabling applications ranging from machine translation to chatbots. But what exactly makes these models so powerful? The key lies in the so-called attention mechanism: it evaluates which words in the text are particularly important for a specific word and thus generates context-dependent embeddings. In this article, you will learn how the attention mechanism works mathematically, why it makes the difference compared to older methods such as LSTM, and where it is used in AI systems today.

What happens when artificial intelligence turns words into numbers? Behind modern language models lies an inconspicuous but central principle: vectorization. It translates language, images, and sounds into mathematical structures, making understanding, searching, and generation possible in the first place. In this article, you will learn what exactly vectorization in AI means, how embeddings are created, why AI systems need numerical representations instead of words, and how these vectors are used today in search engines, RAG systems, and recommendation systems. Using a vivid example from the Middle Ages, I will show step by step how semantic relationships are converted into mathematical objects, from one-hot encoding to Word2Vec to contextual embeddings in modern transformer models.

In a world where crises, upheavals, and uncertainty have become the new normal, traditional leadership tools are no longer sufficient. Routines are breaking down, conflicts are increasing—and suddenly it is no longer the best process that determines success or failure, but people's behavior. This article shows why managers today need to lead differently, what role adaptability and resilience play in this, and how models such as DiSC® can provide guidance without becoming pigeonholed.

How does software remain successful on the market after 25 years? The key lies in good product management: user feedback as a compass and implementation through shared responsibility combined with the incorporation of modern technical and technological trends. Here you can find out how Projektron BCS has developed and remained innovative with this approach since 2001.

German industry is under pressure: global crises, geopolitical uncertainties, disrupted supply chains, and high energy prices are weighing heavily on the automotive sector and small and medium-sized industrial companies in particular. Many companies need to flexibly adjust their production capacities without making long-term personnel decisions. Short-time work offers a proven solution here: it enables temporary reductions in working hours and secures jobs. For employees, however, this often means a loss of income and uncertainty. This makes transparent processes that build trust all the more important. Projektron BCS supports companies in clearly mapping and documenting short-time work from planning to accounting.

Artificial intelligence, and language models such as ChatGPT in particular, are fascinating because of their ability to understand and generate text almost like a human being. But how do these systems manage to process our language, which is full of ambiguities, dialects, neologisms, and special characters? The answer lies in an inconspicuous but crucial step: tokenization. It transforms texts into small units that machines can understand – tokens.