The Calibrated Prompt

Here is the prompt I now use. It implements the three EIC operations directly. Rather than a heuristic, the prompt is a constraint system designed to force traversal of distant conceptual spaces and prevent regression toward known solutions. Each constraint maps to a specific failure mode observed in testing:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41

For the following problem:
[INSERT PROBLEM]

Step 1 — Extraction
Name 8-10 specific, concrete domains FAR from the problem domain.
Choose your own domains — do NOT reuse any domain mentioned
in this prompt. For each domain:
- Name the domain explicitly.
- Describe ONE specific phenomenon in 2-3 sentences.
- Extract the causal rule in the form
  [condition] → [mechanism] → [consequence].
- Compress until no domain-specific vocabulary remains.

TEST: if the compressed rule reads like systems theory, control
theory, or generic organizational advice, you haven't gone far
enough. Start over with a more concrete phenomenon.

Step 2 — Inversion
For each rule, identify 2-3 breakpoints in the causal chain.
For each breakpoint, describe a concrete intervention — not the
negation of the condition, but a structurally different mechanism
that disrupts the link. If your inversion is simply "do the
opposite," it's not an inversion — find a different angle.

Step 3 — Composition
Propose 3-5 recipes that combine inversions from DIFFERENT rules
(each recipe must use at least two). For each recipe:
- Name it.
- Explain how the inversions interact — what does their
  combination produce that neither produces alone?
- A strong composition does not just SOLVE the problem better.
  It REDEFINES what the problem is, what you measure, or what
  counts as success. If your recipe is a process improvement
  or an architecture, push further — what does it reveal about
  the problem itself that no one was seeing?
- Describe a concrete implementation scenario in 3-4 sentences.
- Assess its transformative strength.
- List the constraints it exposes.

Do not filter for current applicability. Maximize transformative
strength.

Every constraint in this prompt maps to a lesson from the theory. The self-test on extraction prevents over-compression to the philosophical-principle level. The “not the negation” rule on inversions prevents mechanical breakpoints. The redefinition instruction on compositions forces the model past the architecture level into genuine reconceptualization. Remove any of these constraints and the output quality degrades measurably.

What Happened When I Tested This

I gave this prompt cold to three different LLMs (ChatGPT, Grok, and Gemini), with no conversation, just the prompt and the technical debt problem. The results genuinely surprised me.

Both models produced legitimate creative output. The extraction worked: concrete domains (glassblowing, commercial beekeeping, bonsai cultivation, ice climbing, jazz band rehearsal), specific phenomena described in detail, compressed rules that were portable without reading like systems theory. The self-test constraint did its job: the models actually traversed distant domains instead of staying in abstract territory.

The inversions were also substantive. Rather than mechanical negations, they were structurally different interventions: “hot-swap modular subsystems as quick-replace cartridges,” “timber sold as regrowth futures contracts that pay bonuses only after next-cycle viability is certified,” “exhibition tree leasing model where operators display but do not own the final specimen.” These are genuinely inventive mechanisms.

The compositions reached the redefinition level. Both models produced statements that reconceptualize the problem rather than solving it incrementally. “Progress is not continuous delivery. Progress is the ability to survive interruption without degradation.” “There is no such thing as technical debt repayment. There is only continuous structural shaping.”

What interested me most, though, was that the two models produced qualitatively different results from the same prompt.

ChatGPT’s compositions were aphoristic and trenchant, tight redefinitions that read almost like proverbs. “Failure is not an event. Failure is a state the system is allowed to approach.” These are memorable and conceptually sharp.

Grok’s compositions were richer in texture and more operational. Where ChatGPT redefined in a sentence, Grok invented economic structures: “regrowth futures contracts,” “colony equity pools,” “maturity tokens that investors trade instead of bottled product.” Grok’s output was less tweetable but more implementable; you could see how to build each idea.

A third model, Gemini, pushed the extraction further still, into forensic taphonomy, dendrochronology, and ballistics, and produced what may be the most radical redefinition of all: the problem isn’t technical debt, it’s premature solidification. Debt is only debt if it’s locked into the final structure. Keep the system in a fluid state until the last possible moment, and the concept of debt dissolves entirely. Where ChatGPT redefined in aphorisms and Grok in economic structures, Gemini redefined through physical-state metaphors: a third creative signature from a third training lineage.

With the same theory, prompt, and problem, I got three different creative profiles. The difference lies not in the quality of the output but in its nature, and that nature is a direct reflection of each model’s training data and architecture. For any serious creative work, then, testing the same prompt across multiple models is essential rather than redundant, because each model will illuminate a different region of the solution space.

The Remaining Gap

The prompt produced results I’d call genuinely creative. But there’s a level it didn’t reach on its own.

Earlier, through sustained iterative conversation, I produced a concept (reproductibility as the metric of code health) that emerged from a specific collision between two specific rules at a specific moment of dialogue. I had noticed that causal rules were diagnostics, said so, and the model pivoted. That pivot, and the insight it produced, didn’t come from the prompt. It came from a human observing an intermediate result, naming what was wrong with it, and redirecting.

The prompt handles extraction and inversion reliably. It pushes composition to the redefinition level. But the highest-order insights, the ones that emerge from the friction between a human’s situated judgment and a model’s combinatorial power, still require iterative dialogue. The prompt is the instrument’s score; the conversation is the performance.

This has implications beyond creative brainstorming. The current industry push toward autonomous AI agents rests on the assumption that if the specification is good enough, the agent can execute independently. A spec, however detailed, is a frozen prompt. It captures intent at a point in time. What it cannot capture is the judgment that happens during execution: the moment when a human looks at an intermediate result and says “this is too abstract” or “you’re recombining known patterns” or “go further.” That judgment acts as a steering mechanism throughout, not as a quality check at the end.

This doesn’t mean autonomous agents are useless. They are interpolation engines, excellent for well-defined, bounded tasks where the acceptable output space is known in advance. But for any task that requires genuinely novel output (creative problem-solving, architectural invention, strategic design), the human in the loop is the creative engine rather than a safety net, and the LLM is the fuel.

Angine de Poitrine and the Universality Test

I developed the EIC framework through IT problems: technical debt, organizational architecture, QA strategy. But a theory of creativity that only works in engineering isn’t much of a theory. So I tested its limits against the most unlikely case I could find.

In April 2026, the masked math-rock duo Angine de Poitrine from Chicoutimi became the most viral act on Spotify worldwide. They play microtonal rock with polyrhythmic structures, wear papier-mâché masks, sing in an invented language, and describe themselves as a “Mantra-Rock Dada Pythagorean-Cubist Orchestra.” Their success is improbable by every industry metric, yet the model accounts for it.

Underneath their buzz lies a causal chain that no one has formalized. The music industry has been optimizing its output toward a statistical center for decades, through the formulas, timbres, and structures that maximize engagement. Generative AI accelerated this trend to a saturation point. As optimized content proliferates, the public loses its sense of human authenticity in what it hears. That erosion creates a need (not conscious, not articulated, but real) to reaffirm an identity distinct from the machine. And that need translates into a valorization of the improbable: the more something is manifestly non-optimizable, the more it functions as proof of humanity.

That’s a causal chain: [optimization saturation → authenticity erosion → need for human identity → valorization of the improbable]. You can extract it, compress it, invert it at different links.

But the composition thesis is what Angine de Poitrine validates most clearly. None of their ingredients is new. Math-rock has existed since the 1990s. Microtonal music since Charles Ives. Stage masks since the Residents. Dada aesthetics since 1916. Repetitive loops since minimalism. What is new is the recipe: the specific combination of these elements, in this order, with this attitude, at this precise cultural moment. No critic could have predicted that these ingredients, assembled this way, would produce a global phenomenon. The invention lies in the assembly.

They also validate the fifth finding most powerfully of all. The duo never filtered their art to fit the existing industry constraints. They didn’t ask whether instrumental microtonal rock sung in an invented language was “commercially viable.” By existing in authentically non-conforming fashion, they exposed that the industry had optimized humanity out of its own music. They succeeded because of their improbability, not despite it, and revealed the constraints of the system by refusing to submit to them.

Limitations

The EIC framework has boundaries I want to be explicit about.

The quality of extraction depends entirely on whether the model actually traverses distant domains or takes the path of least statistical resistance. The prompt constraints help, but they don’t guarantee it, and different models respond differently to the same constraints. There is no automatic way to verify that a compressed rule is genuinely novel rather than systems theory in disguise. That verification still requires human judgment.

Evaluating composition quality is harder still. I can recognize that “reproductibility as the metric of code health” is a redefinition rather than a process improvement. But I can’t formalize why I recognize it, which means I can’t automate the evaluation. Any benchmark built on this framework will require human evaluators, with all the subjectivity that implies.

The framework is strongest in domains with identifiable causal structures, such as engineering, organizational design, strategy, and medicine. I tested it against a cultural phenomenon (Angine de Poitrine) and the model held, but the causal structure there is in the system surrounding the artifact, not in the artifact itself. For domains where causality is less clear (pure art, fundamental research, philosophy), the framework may not apply, or may apply only partially.

Finally, the iterative conversation still produces results that the autonomous prompt does not. I’ve been honest about this throughout the article, but it bears repeating as a limitation: the EIC framework as a prompt is a useful instrument, but it is not a replacement for sustained human-LLM dialogue.

Then and Now

For a long time I thought creativity lived primarily in the metaphorical transfer, the initial connection between distant domains. LLMs gave me an experimental instrument I didn’t have before. By automating extraction at scale, they made visible what was hidden: transfer is preparatory. The real creative work happens in the composition.

One last thought. Beyond brainstorming, the prompt in this article can serve as a measurement instrument. Apply the same prompt to the same problem on different models and compare the results along three axes: depth of extraction, originality of inversions, and level of redefinition in compositions. My tests showed that different models produce qualitatively different creative profiles from identical inputs, and those differences are themselves interesting data. If researchers want to formalize a benchmark for computationally-assisted creativity, the EIC framework is designed to serve as a starting point.

The most powerful ideas are not the ones that adapt neatly to our current world, but the ones that make us willing to change it. Now I know what to look for.

But one question wouldn’t leave me alone. The EIC framework works forward, from problem to solution. But if causal rules are truly portable across domains, the framework should work in the other direction too. What happens when you start from a solution instead of a problem? That’s Part IV.

Gabriel Mailhot is a Senior Subject Matter Expert at Desjardins, where he works on development practices, architecture, and complex organizational problems spanning technology, process, and governance. His 2003–2010 research focused on creative systems and metaphor-based retrieval. He publishes at scripting.pro.