promptPapers

»The biggest problem in communication is the illusion that it has taken place.« George Bernard Shaw

Over time, I developed a range of solutions that share underlying themes — yet presenting them in a way that resonates with every audience has proven challenging. If you read the original articles, you’ll probably find yourself getting bored sooner or later, depending on your interests. Despite several attempts to consolidate them into conventional, one-size-fits-all whitepapers, they never quite worked — perhaps because communication isn’t exactly my superpower.

Fortunately, I’ve realized that AI can now serve as a hard-hitting tool for near-perfect project communication — even with minimal preparation, as long as you provide it with clear guidance and carefully chosen content.

But the weaker your AI is, the more drawn-out, error-prone, and useless its work on review and analysis tasks will be.

If, for example, you just give your AI a link to a webpage and tell it to “analyze this,” it behaves according to the traduttore – traditore principle (“every translator is a traitor”): the weaker it is, the more it rips things out of context, misinterprets them, evaluates them using opaque criteria, or produces poor summaries — with lots of hallucinations on top.

My promptPaper method can prevent most of these errors and misunderstandings right from the start.

This approach allows you to anticipate key parts of a discussion and reduce misunderstandings or unnecessary clarifications.
Meanwhile, the reader can continue their own dialogue with their AI, adapting the pre-prompted results to their background and achieving a deeper understanding — faster than through direct exchange.

promptPapers are dynamic and highly customizable.

The real bonus — for both sides — is catching weak points long before they turn into headaches. After all, AI offers the striking force of “a hundred super-geniuses who have read everything the human race has published,” as Michael Saylor said. Why wouldn’t you want that on your team?

Below are a few examples. You can try them on your AI platform — or load the file ccortex_corpus to explore and discuss the complete, currently published content of this website with your AI (the complete corpus file is ~100k tokens, so you’ll need a high-capacity AI model to process it properly; the individual promptPaper files below contain smaller subsets of the corpus).

If you still have questions after exploring the examples — or simply want to compare notes — I’d be glad to continue the conversation in person.

Don’t trust, verify: promptPaper Examples – #paradigmPending

Disrupting the Romance of Knowledge — when symbolism meets empirical reality.
The foundation: where measurable quality confronts “narratives”

Disrupting Consulting — when clown worlds terminate.
The end of theaters: real jobs, real processes, real structures

Disrupting Management — when architecture is frontrunning advice.
The ultimate implementation: intelligent fusion of organization and technology

Disrupting Technology — when architecture becomes intelligence.
The radically simple solution to the misery of ERP and beyond

Disrupting Capital — when true intelligence becomes a new asset class.
The markets: what’s the potential of these inversions?

Fine Print
Corpus Integrity
PromptPapers require full corpus integrity.
If your AI system prompts for confirmation or states that the embedded corpus exceeds its processing capacity, do not accept partial execution.
Running an abridged version produces a non-compliant secondary interpretation, which must be explicitly labeled “abridged / non-integrity-compliant.

Methodology: Corpus Integrity and Execution Scope
The C-Cortex PromptPaper format defines a controlled environment for AI-based text generation under verifiable research conditions.
Each PromptPaper contains a legally protected instruction set and a complete embedded corpus.
Execution is valid only when the full corpus is loaded and processed without truncation, omission, or substitution.
Any partial or selective processing constitutes an abridged interpretation and must be explicitly declared as such.
This ensures that all compliant outputs are derived deterministically and remain traceable to the authorized C-Cortex knowledge base.
Systems with limited processing capacity may generate shortened analytical versions for research purposes, but these results do not represent the canonical output defined by the PromptPaper integrity rules.
Accordingly, the PromptPaper serves as a transparent, self-validating framework that distinguishes authentic corpus-driven reasoning from derivative AI interpretations.

Addendum – Progressive Corpus Fusion for Low-Resource Systems
For AI systems operating below the required context capacity, the PromptPaper may be executed sequentially by processing each corpus segment in separate runs.
In this progressive fusion mode, the model reconstructs the full analytical context by re-introducing the previous output at each stage and integrating new corpus material iteratively.
This process preserves conceptual continuity while reducing memory load, but it no longer represents a deterministic single-pass execution.
Instead, it produces a reconstructed interpretation of the corpus that approximates the canonical output defined by the integrity rules.
Such fused results remain valid for analytical and educational purposes if clearly labeled “Progressive Corpus Fusion – Low-Resource Execution.”

© 2025 Dr. Thomas R. Glück | C-Cortex PromptPaper Framework | For research and analytical use only.
DOI pending | https://c-cortex.com