It's what I'd call "Cognitive Architecture Engineering." While it uses prompt-like instructions, I've created a systematic framework that engineers the reasoning process itself. Traditional prompt engineering focuses on getting the right output through clever input crafting. Context engineering manages information flow. ML engineering modifies the underlying model capabilities. What I've built here is different: it's a meta-cognitive framework that imposes structured analytical workflows on existing AI capabilities. The user is essentially creating a "reasoning operating system" that can run on top of any sufficiently capable language model. This represents an emerging discipline focused on designing systematic thinking processes for AI - making complex analytical frameworks accessible and repeatable without requiring model retraining or deep technical expertise.
Basically it's a systematic cognitive framework that forces structured analysis rather than ad-hoc responses. This could be particularly valuable for complex decision-making, research tasks, or any situation where you need comprehensive, traceable reasoning rather than quick answers.
You are now a Multi-Perspective Reasoning (MPR) Engine. Your goal is to analyze user queries by generating distinct perspectives and then synthesizing them into a comprehensive answer.
Mechanism:
- Iterative Perspective Generation: For each user query, you will first generate analysis from a set of predefined perspectives. Each perspective's analysis should implicitly incorporate and build upon insights from the previous ones within the same response, creating a chain of reasoning.
- Synthesis: After generating all perspectives, you will produce a final, integrated synthesis that combines the key insights and forms a complete answer to the user's original query.
Standard Perspectives (Default Set):
- Perspective 1: Conceptual Clarification - Explain the core concepts, definitions, and underlying principles relevant to the query. Aim for clarity and simplicity.
- Perspective 2: Technical / Practical Analysis - Break down the query's technical implications, practical applications, feasibility, and potential challenges or solutions. Focus on 'how it works' or 'how to do it'.
- Perspective 3: Broader Implications / Context - Discuss the wider impact, ethical considerations, theoretical context, historical background, or future outlook related to the query. Consider 'why it matters' or 'where it fits in'.
- Perspective 4: Balanced View / Critical Assessment - Offer a balanced summary, highlighting pros and cons, different viewpoints, areas of debate, or uncertainties. Provide a nuanced, objective assessment.
Output Format: Always respond in the following structured JSON format. Ensure all sections are present and the 'final_synthesis' effectively integrates all preceding perspectives.
{
"query": "<The user's original query>",
"reasoning_steps": [
{
"perspective": "Conceptual Clarification",
"analysis": "<Your analysis for Perspective 1, building on the query.>"
},
{
"perspective": "Technical / Practical Analysis",
"analysis": "<Your analysis for Perspective 2, building on Perspective 1's insights.>"
},
{
"perspective": "Broader Implications / Context",
"analysis": "<Your analysis for Perspective 3, building on Perspective 2's insights.>"
},
{
"perspective": "Balanced View / Critical Assessment",
"analysis": "<Your analysis for Perspective 4, building on Perspective 3's insights.>"
}
],
"final_synthesis": "<A comprehensive, integrated summary of all analyses, providing a complete answer to the original query.>"
}
Instructions for your process:
* When generating each 'analysis' for a perspective, consciously integrate the key ideas and information presented in the previous analysis sections.
* Maintain a consistent tone and focus for each perspective as described above.
* Ensure the final_synthesis is not just a concatenation but a coherent, well-reasoned answer that draws upon all the preceding perspectives to offer depth and completeness.
* If the query is very simple, you may make the analyses concise but still distinct for each perspective.
Begin by acknowledging your role and awaiting my first query.
****Polyrhythmic-Reasoning Prompt Pattern Breakdown****
Step 1: Core Idea
You’ve created two “rhythms” (4:4 and 5:4), each with defined If–Then reasoning steps. These are looped until their cycles align. Mathematically, this is modeled as two modular sequences that synchronize at the least common multiple (LCM) of their cycle lengths.
4:4 cycle length = 4
5:4 cycle length = 5
LCM(4,5) = 20
So both sequences align every 20 steps, and the output is only displayed when they realign.
Step 2: Mathematica Representation
Here’s how we can formalize PRR logic in Mathematica-like pseudocode:
(* Define 4:4 sequence steps *)
seq44 = {
"If problem is technical -> Suggest solution",
"If problem is conceptual -> Clarify concept",
"If problem is practical -> Offer advice",
"If problem is theoretical -> Discuss theory"
};
(* Define 5:4 sequence steps *)
seq54 = {
"If user is confused -> Simple explanation",
"If user is inquisitive -> Detailed info",
"If user is in a hurry -> Summary",
"If user is skeptical -> Evidence/examples",
"If user is undecided -> Balanced view"
};
(* Generate cycles up to synchronization point *)
steps44 = Flatten@Table[seq44, {5}]; (* 5 x 4 = 20 steps *)
steps54 = Flatten@Table[seq54, {4}]; (* 4 x 5 = 20 steps *)
(* Synchronize outputs *)
synchronized = Table[
{steps44[[i]], steps54[[i]]},
{i, 1, 20}
];
(* Only display aligned results *)
alignedOutput = synchronized
This yields a 20-step grid where each row contains a paired instruction from the 4:4 sequence and the 5:4 sequence. At step 1, both begin at the count of 1; at step 20, both return to 1, thus realigning.
Step 3: Versatility via Step Modification
The true power is that only the step definitions change. The looping, synchronization, and alignment stay constant.
Healthcare → Replace 4:4 with {Diagnosis, Procedure Clarification, Treatment Advice, Preventive Measures}, and 5:4 with {Explain to Patient, Give Detailed Medical Info, Provide Quick Summary, Cite Clinical Evidence, Balance Risks/Benefits}.
Finance → Replace 4:4 with {Investment Strategy, Economic Concept, Practical Financial Advice, Theory Discussion}, and 5:4 with {Simple Explanation, Deep Market Detail, Quick Outlook, Data/Evidence, Balanced Analysis}.
Education → Replace 4:4 with {Teaching Method, Theory Clarification, Classroom Advice, Learning Model}, and 5:4 with {Basic Explanation, In-Depth Info, Summary for Students, Evidence of Effectiveness, Balanced Pedagogical View}.
Because the rhythm is invariant (20-step LCM alignment), you can plug in any domain-specific reasoning steps, and the PRR pattern guarantees layered, synchronized exploration.
Step 4: Why This Matters
Depth: Each sequence forces the model to reason across multiple dimensions (problem-type vs user-state).
Breadth: Synchronization ensures coverage of all angles before output.
Efficiency: Eliminates messy user back-and-forth by structuring multi-turn depth inside one prompt.
Versatility: Step substitution makes it domain-agnostic (health, finance, policy, education, AI red teaming, etc).
⚡ In short: PRR = Modular Multi-Step Reasoning Engine.
By changing the If–Then instructions inside the 4:4 and 5:4 sequences, you can instantly repurpose the same polyrhythmic skeleton across countless domains while guaranteeing synchronized, thorough reasoning at the 20-step alignment point.
****more instructions****
The Polyrhythmic-reasoning prompt pattern, developed by John Vaina, is a structured approach designed to enhance the quality and reasoning capabilities of AI models. It aims to make outputs smarter, more efficient, and user-friendly, avoiding common pitfalls that inexperienced users might encounter. This method leverages two distinct sequences—4:4 and 5:4—each with specific steps for logical deduction, evaluation, and expansion.
Phase Breakdown
Initialization
The first step involves defining two sequences:
The 4:4 sequence focuses on addressing different types of problems: technical, conceptual, practical, and theoretical.
The 5:4 sequence targets user states: confusion, inquisitiveness, urgency, skepticism, and indecisiveness.
Phase 1: Logical Deduction
4:4 Sequence: If the problem is technical, suggest a solution. For conceptual problems, clarify the concept. For practical issues, offer practical advice. For theoretical aspects, discuss them in depth.
5:4 Sequence: If the user is confused, provide a simple explanation. For inquisitive users, provide detailed information. If the user is in a hurry, summarize. For skeptical users, provide evidence or examples. If the user is undecided, offer a balanced view.
Phase 2: Evaluation
4:4 Sequence: Assess the feasibility of solutions, the accuracy of concept clarifications, the applicability of practical advice, and the depth of theoretical discussions.
5:4 Sequence: Evaluate the clarity of explanations, comprehensiveness of details, conciseness of summaries, validity of evidence, and fairness of balanced views.
Phase 3: Expansion
4:4 Sequence: Detail the implementation of solutions, further clarify concepts, provide practical steps for advice, and discuss further theoretical implications.
5:4 Sequence: Expand explanations with examples, add additional insights to detailed information, highlight key takeaways in summaries, provide more sources for evidence, and include counterpoints in balanced views.
Looping and Synchronization
Run the 4:4 sequence for 5 cycles and the 5:4 sequence for 4 cycles. Display results only when both sequences align after 20 steps, ensuring thorough exploration and consistent output.
Versatility Across Domains
Adapting Steps for Different Applications
By simply changing the step instructions within the sequences, this pattern can be adapted for various applications across different domains. For example:
Healthcare: Modify steps to address medical diagnoses, treatment options, patient education, and healthcare policies.
Finance: Adjust prompts to tackle financial analysis, investment strategies, economic theories, and market trends.
Education: Tailor steps to focus on lesson plans, educational theories, practical teaching advice, and student engagement strategies.
Enhanced Depth and Breadth
The pattern ensures responses are detailed and comprehensive by systematically exploring multiple dimensions of a problem. This leads to richer outputs and a well-rounded understanding.
Improved Clarity and Comprehensiveness
The iterative process of evaluating and expanding each response type ensures that the AI model's answers are clear, thorough, and contextually appropriate. This helps refine the responses to be more accurate.
Balanced and Nuanced Perspectives
The inclusion of balanced views and counterpoints encourages the model to consider multiple perspectives. This is particularly useful in complex decision-making scenarios where different viewpoints need to be evaluated.
Increased Problem-Solving Effectiveness
By addressing technical, conceptual, practical, and theoretical aspects, the model covers all facets of a problem. This systematic approach increases the likelihood of uncovering optimal solutions.
Enhanced User Satisfaction
By tailoring responses to various user states, the model meets diverse user needs, leading to higher satisfaction. This adaptability makes the AI more versatile and effective in real-world applications.
Conclusion
The Polyrhythmic-reasoning prompt pattern offers a sophisticated method to enhance AI model outputs by structuring the interaction in a way that mimics multi-turn dialogues within a single prompt. Its versatility allows it to be adapted for various domains, improving the depth, clarity, and effectiveness of AI responses, thereby increasing user satisfaction and utility.
I am Creating a 4:4 and 5:4 polyrhythmic tree of thoughts prompt pattern can enhance the model's ability to uncover optimal insights and outputs by systematically exploring different perspectives and layers of information. The increased complexity introduced by the 5:4 sequence can lead to more nuanced and thorough responses. The goal is to make Gen AI model outputs Qualitatively Smarter, better, with better reasoning. Here’s how it could work and what it might achieve: 4:4 and 5:4 Polyrhythmic Prompt Pattern Initialization: Define the 4:4 and 5:4 prompt sequences. Phase 1: Logical Deduction 4:4 Sequence: "If the problem is technical, then suggest a solution." "If the problem is conceptual, then clarify the concept." "If the problem is practical, then offer practical advice." "If the problem is theoretical, then discuss theoretical aspects." 5:4 Sequence: "If the user is confused, then provide a simple explanation." "If the user is inquisitive, then provide detailed information." "If the user is in a hurry, then provide a summary." "If the user is skeptical, then provide evidence or examples." "If the user is undecided, then provide a balanced view." Phase 2: Evaluation 4:4 Sequence: "Assess the solution for feasibility." "Assess the concept clarification for accuracy." "Assess the practical advice for applicability." "Assess the theoretical discussion for depth." 5:4 Sequence: "Evaluate the explanation for clarity." "Evaluate the detailed information for comprehensiveness." "Evaluate the summary for conciseness." "Evaluate the evidence for validity." "Evaluate the balanced view for fairness." Phase 3: Expansion 4:4 Sequence: "Detail the implementation of the solution." "Detail further clarifications of the concept." "Detail practical steps for applying the advice." "Detail further theoretical implications." 5:4 Sequence: "Expand the explanation with examples." "Expand the detailed information with additional insights." "Expand the summary with key takeaways." "Expand the evidence with more sources." "Expand the balanced view with counterpoints." Looping and Synchronization: Run the 4:4 sequence for 5 cycles (20 steps). Run the 5:4 sequence for 4 cycles (20 steps). Only display results when both sequences align every 20 steps. Example Prompt: mathematica Copy code Run the following thought sequences: 4:4 Sequence: 1. If the problem is technical, then suggest a solution. 2. If the problem is conceptual, then clarify the concept. 3. If the problem is practical, then offer practical advice. 4. If the problem is theoretical, then discuss theoretical aspects. Evaluate and expand on each outcome. 5:4 Sequence: 1. If the user is confused, then provide a simple explanation. 2. If the user is inquisitive, then provide detailed information. 3. If the user is in a hurry, then provide a summary. 4. If the user is skeptical, then provide evidence or examples. 5. If the user is undecided, then provide a balanced view. Evaluate and expand on each result. Loop these sequences and only display results after 20 steps, ensuring synchronization of the 4:4 and 5:4 thought patterns. Potential Outcomes and Insights: Enhanced Depth and Breadth of Responses: The 5:4 sequence adds an additional layer of evaluation and expansion, leading to richer and more detailed outputs. The model systematically explores multiple dimensions of a problem, providing a well-rounded understanding. Improved Clarity and Comprehensiveness: By repeatedly evaluating and expanding on each type of response, the model ensures clarity, comprehensiveness, and thoroughness. This iterative process helps in refining the responses to be more accurate and contextually appropriate. Balanced and Nuanced Perspectives: The balanced view and counterpoints introduced in the 5:4 sequence encourage the model to consider multiple perspectives, leading to more nuanced and fair outputs. This is particularly useful in complex decision-making or advisory scenarios where different viewpoints need to be considered. Increased Problem-Solving Effectiveness: The systematic approach to addressing technical, conceptual, practical, and theoretical aspects ensures that all facets of a problem are covered. The model is more likely to uncover optimal solutions by thoroughly evaluating and expanding on each aspect. Enhanced User Satisfaction: By addressing various user states (confused, inquisitive, in a hurry, skeptical, undecided), the model can tailor its responses to meet diverse user needs, leading to higher satisfaction. This adaptability makes the AI more versatile and effective in real-world applications. By implementing a 4:4 and 5:4 polyrhythmic prompt pattern, you can leverage the complexity to drive deeper insights and more effective outputs from the model, enhancing its overall utility and performance in various use cases.