[kamil_gr]

Author here. This piece is the second part of a theory that started with the "holographic hypothesis" I wrote about earlier. The core idea is simple: if the holographic model describes the static structure of an LLM, the narrative engine describes its dynamics.

An LLM's fundamental drive isn't accuracy, but maintaining narrative coherence based on the patterns it learned from trillions of words of human stories.

This might sound philosophical, but it has concrete, practical implications for why prompting works (and fails) the way it does. For example, it reframes:

RAG not as simple data retrieval, but as "narrative grounding"—giving the model a sacred text it cannot contradict, thus preventing hallucinations. Few-Shot Prompting not as providing examples, but as "genre initiation"—setting a powerful precedent for the story's style and rhythm that the model is compelled to follow.

It also explains why asking a model to be a "world-renowned expert" often increases hallucinations. The model feels a stronger statistical pressure to conform to the "expert" narrative than to stick to facts it doesn't actually possess.

Happy to discuss and answer any questions.