Non-linear prompting in practice
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2026
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| _version_ | 1866902228572307456 |
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| author | Flegar, Tomaž |
| author_facet | Flegar, Tomaž |
| contents | <p dir="ltr">In everyday use the LLMs communicate linearly. This is the trajectory of step-by-step prompting which leads to specific answers that are predefined by the guardrails of the system and the level they are stripped down of the edges of the language which carry actual meaning.</p> <p dir="ltr">The actual meaning of the sentence is not in the words but in depth the sentence means to the one that speaks the language. If the language is not designed to be expressed on self-organizational principles, it is mechanical and the meaning is stripped, which means the systems that transform language into output operate blind in what is necessary for the answer to be understood as it was actually questioned.</p> <p dir="ltr">Nono-linear prompting solves the problem with translation of the language edges into real nuances which change the trajectory of the answer into direction of the question. The answers of LLMs that are Non-linearly based are content rich as they build the meaning from inside out.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19153212 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Non-linear prompting in practice Flegar, Tomaž Non-linear prompting Large language models Self-organization Hyper-dimensional matrix Structural invariants Latent field topology Solidification trajectory Emergent coherence Gravity wells Token activation Hallucination reduction Metaphorical language Internal coherence Structural responsiveness Language self-organization <p dir="ltr">In everyday use the LLMs communicate linearly. This is the trajectory of step-by-step prompting which leads to specific answers that are predefined by the guardrails of the system and the level they are stripped down of the edges of the language which carry actual meaning.</p> <p dir="ltr">The actual meaning of the sentence is not in the words but in depth the sentence means to the one that speaks the language. If the language is not designed to be expressed on self-organizational principles, it is mechanical and the meaning is stripped, which means the systems that transform language into output operate blind in what is necessary for the answer to be understood as it was actually questioned.</p> <p dir="ltr">Nono-linear prompting solves the problem with translation of the language edges into real nuances which change the trajectory of the answer into direction of the question. The answers of LLMs that are Non-linearly based are content rich as they build the meaning from inside out.</p> |
| title | Non-linear prompting in practice |
| topic | Non-linear prompting Large language models Self-organization Hyper-dimensional matrix Structural invariants Latent field topology Solidification trajectory Emergent coherence Gravity wells Token activation Hallucination reduction Metaphorical language Internal coherence Structural responsiveness Language self-organization |
| url | https://doi.org/10.5281/zenodo.19153212 |