Author Archives: dirk.bangel@gmx.de

Large language models and agentic systems appear to benefit experienced engineers far more than they help less experienced ones. A useful analogy is that LLMs resemble an exceptionally fast sous-chef rather than a professional head chef, one who has memorized … Continue reading →

For the past few years, AI has resembled a gold rush. Organizations hurried to dump everything they owned, documents, tickets, wikis, logs, into vector databases, convinced that semantic retrieval would finally make large language models dependable. Retrieval-Augmented Generation (RAG) became … Continue reading →

LLMs and agents seem to serve experts much more than they benefit others. One common comparison that is drawn is that LLMs are less likely to be a professional chef than an incredibly fast sous-chef, able to see every recipe … Continue reading →

For a long time, AI research has followed two main paths. These ideas gave us today’s language models and image generators. But they hit limits. They struggle with real understanding, long-term planning, and uncertainty. They copy patterns well, but they … Continue reading →

AI hallucination is defined as when a generative model creates outputs that sound plausible but are in reality false, nonsensical, or unconnected to reality. LLM Modern generates text, for instance, via a probabilistic next-word prediction mechanism. At base, these models … Continue reading →

In today’s world, modern AI is often perceived as a kind of miracle and is frequently mistaken for magic. In reality, however, this power is based on clear architectural principles. To make these concepts tangible, I translate each of them … Continue reading →

With the constantly transforming landscape of artificial intelligence, Large Language Models (LLMs) are a remarkable step within the age of artificial intelligence. But, how do we take user interactions and put them in good use of scarce GPU resources and … Continue reading →

In a world where technology advances at breakneck speed, the evolution of artificial intelligence (AI) offers a compelling glimpse into the future of human–machine collaboration. AI is no longer confined to answering questions or automating repetitive tasks. Instead, it is … Continue reading →

Sequence-to-sequence processing is machine learning in which a chain of information e.g., words is translated into another e.g., for language translation, a chat bot and image generation. Suppose you tell the computer the sentence, “When was Christopher Columbus born?” The … Continue reading →