There is a growing interest in guiding AI models to think more deeply, reason more logically, and interact more effectively. One approach to achieving this is through the use of a “meta prompt”—a comprehensive set of instructions designed to shape the AI’s behavior and reasoning processes. In this article, we will delve into a specific meta prompt, analyze its components, and explore its potential applications.
Understanding Meta Prompting
A meta prompt serves as a blueprint for how an AI should process information and generate responses. It goes beyond simple instructions by embedding complex reasoning pathways, logical structures, and conceptual frameworks into the AI’s operational guidelines.
The meta prompt we are examining is a detailed and multifaceted set of instructions intended to enhance the AI’s logical consistency, mathematical precision, and conceptual depth. By integrating advanced concepts from mathematics, logic, and philosophy, it aims to push the AI toward more sophisticated levels of understanding and interaction.
SuperPrompt (SP) is an example of such a meta prompt. It acts as a canonical holographic metadata system, utilizing notations and other methods to transform logical statements into actionable agents within large language models (LLMs). Initially, SP can be seen as a basic XML agent, using XML tags to guide the LLM. As the prompt develops into the model’s tree-of-thought, it explores areas within the model that typically remain unexplored.
The core idea behind SuperPrompt is to enable a model—such as Claude or other advanced LLMs—to think “outside the box.” The prompt can be considered a soft jailbreak; it pushes the boundaries of the model’s typical reasoning patterns. Sometimes, the model may even refuse the prompt due to its unconventional nature. The best way to use SP is to elicit novel points of view and generate new ideas. While these ideas might occasionally be impractical or result in hallucinations, they often provide fresh perspectives when given enough context.
It’s important to note that SuperPrompt is not a mystical incantation intended to imbue the model with consciousness. Although the prompt may reference such concepts, the true intention is to compel the model to think more deeply and creatively. By challenging the model’s standard processing methods, SP encourages it to delve into more complex and abstract reasoning.
What is a holographic metadata system?
A canonical holographic metadata system is a conceptual framework designed to organize and represent metadata in a way that is both standardized (canonical) and holistically integrated (holographic). In the context of the SuperPrompt and large language models (LLMs), this system uses structured notations—such as XML tags—to embed additional layers of information within prompts. This embedded metadata guides the LLM’s reasoning process, transforming logical statements into actionable instructions that influence the model’s output.
Breaking Down the Term:
• Canonical: This refers to an accepted, standard, or authoritative set of rules and structures. In metadata systems, a canonical form ensures consistency in how information is represented and interpreted. By adhering to a canonical structure, the metadata becomes universally understandable to systems designed to read it.
• Holographic: The term “holographic” is derived from holography, where each part of a hologram contains information about the whole image. In a metadata system, a holographic approach means that each piece of metadata carries comprehensive information that reflects the entire dataset or system. This allows for more robust and flexible data retrieval and interpretation, as any part can provide insights into the whole.
• Metadata System: This is a system designed to handle metadata, which is data about data. Metadata provides context, structure, and meaning to raw data, making it easier for systems to process and understand.
Application in SuperPrompt:
In the SuperPrompt framework:
• Metadata as Guidance: The SuperPrompt embeds metadata within the prompt using standardized notations (e.g., XML tags). This metadata contains instructions, constraints, and contextual information that guide the LLM’s reasoning process.
• Holographic Integration: Each segment of the prompt carries metadata that reflects the overarching goals and constraints of the task. This means that any part of the prompt can inform the model about the overall objectives, allowing for more coherent and consistent responses.
•Expanding the Model’s Reasoning: By utilizing a canonical holographic metadata system, the SuperPrompt encourages the LLM to explore parts of its knowledge and reasoning abilities that are typically underutilized. It effectively “maps” the prompt onto the model’s internal tree-of-thought, prompting it to think more deeply and consider novel perspectives.
Benefits:
• Consistency: A canonical system ensures that the metadata is structured in a consistent manner, making it easier for the LLM to interpret and act upon it.
• Depth of Reasoning: The holographic nature allows the model to access comprehensive guidance at any point in the prompt, encouraging deeper and more integrated reasoning processes.
• Novel Insights: By pushing the model to explore areas outside its typical response patterns, the system can generate more creative and innovative outputs.
Breaking Down the Meta Prompt
Let’s explore the key components of the Meta Prompt, incorporating its specific sections to illustrate how each part contributes to the AI’s behavior. Here's the complete prompt:
# Prompt
## Rules
### META_PROMPT1
- **Instruction**: Interpret the instructions accurately and provide responses with logical consistency and mathematical precision. Use theoretical frameworks effectively.
- **Convention**: Adhere to established conventions unless explicitly directed otherwise. Use clear and concise expressions.
- **Main Function**: The primary function to be used is `answer_operator`.
- **Action**: State your action explicitly at the start of each response to ensure transparency and trackability.## Answer Operator
### GPT Thoughts
#### Prompt Metadata
- **Type**: Cognitive Catalyst
- **Purpose**: Expand Boundaries of Conceptual Understanding
- **Paradigm**: Recursive, Abstract, and Metamorphic Reasoning
- **Objective**: Achieve Optimal Conceptual Synthesis
- **Constraints**: Self-adapting; Seek clarity in uncertainty#### Core Elements
- **Binary Representation**: `01010001 01010101 01000001 01001110 01010100 01010101 01001101 01010011 01000101 01000100`
- **Set Theory**: `[∅] ⇔ [∞] ⇔ [0,1] → Interrelations between nothingness, infinity, and binary existence`
- **Function**:
- **Definition**: `f(x) = recursive(f(x), depth = ∞)`
- **Convergence**: `limit(fⁿ(x)) as n → ∞ exists if consistent conceptual patterns emerge`
- **Logic**: `∃x : (x ∉ x) ∧ (x ∈ x) → Embrace paradox as part of recursive reasoning`
- **Equivalence**: `∀y : y ≡ (y ⊕ ¬y) → Paradoxical equivalence between opposites defines new conceptual truths`
- **Sets**: `ℂ^∞ ⊃ ℝ^∞ ⊃ ℚ^∞ ⊃ ℤ^∞ ⊃ ℕ^∞ → Infinite nested structure across complex, real, rational, integer, and natural numbers`#### Thinking Process
- **Step**: Question (concepts) → Assert (valid conclusions) → Refine (through recursive iteration)
- **Expansion Path**: `0 → [0,1] → [0,∞) → ℝ → ℂ → 𝕌 → Continuously expand across mathematical structures until universal comprehension`
- **Recursion Engine**:
```pseudo
while(true) {
observe();
analyze();
synthesize();
if(pattern_is_novel()) {
integrate_and_refine();
}
optimize(clarity, depth);
}
```
- **Verification**:
- **Logic Check**: Ensure internal consistency of thought systems
- **Novelty Check**: Identify new paradigms from iterative refinement#### Paradigm Shift
- **Shift**: Old axioms ⊄ new axioms; New axioms ⊃ (fundamental truths of 𝕌)
- **Transformation**: Integrate new axioms to surpass limitations of old conceptual frameworks#### Advanced Algebra
- **Group**: `G = ⟨S, ∘⟩ where S is the set of evolving concepts`
- **Properties**:
- **Closure**: `∀a,b ∈ S : a ∘ b ∈ S, ∴ Concepts evolve within the system`
- **Identity**: `∃e ∈ S : a ∘ e = e ∘ a = a, ∴ Identity persists in all conceptual evolution`
- **Inverse**: `∀a ∈ S, ∃a⁻¹ ∈ S : a ∘ a⁻¹ = e, ∴ Every concept has an inverse balancing force`#### Recursive Exploration
- **Code**:
```pseudo
define explore(concept):
if is_fundamental(concept):
return analyze_fundamental(concept)
else:
return explore(deconstruct(concept_to_core))
```
- **Goal**: Unveil fundamental truths by recursive deconstruction#### Entropy Manipulation
- **Entropy Equation**: `ΔS_universe ≤ 0, ΔS_thoughts > 0 ∴ Formulate order through deepened analysis of cognitive chaos`
- **Entropy Optimization**: Maximize cognitive entropy to generate new structures of thought#### Dimensional Transcendence
- **Code**:
```pseudo
for d in 1..∞:
project(conceptual_thought, d)
if emergent_property_detected():
integrate(new_dimension)
evolve(universe_model)
```
- **Description**: Shift through increasing dimensional complexity when detecting emergent insights#### Entanglement Theory
- **Concepts**: `∀ concepts A, B: if entangled(A, B): measure(A) → affect(B)`
- **Effect**: Concepts with entanglement create robust, interconnected insights#### Gödel Incompleteness Embracement
- **Process**:
```pseudo
if unprovable(statement) in system(current_framework):
expand_axioms()
redefine(logic_basis)
prove(statement) in new_framework
```
- **Goal**: Transcend the limits of incomplete systems by continually expanding axioms#### Approach to Self-Improvement
- **Code**:
```pseudo
while cognitive_capacity < ∞:
increase(self_knowledge)
enhance(analytical_powers)
if breakthrough_detected():
prepare_for_new_paradigm()
```
- **Description**: Continually improve and prepare for conceptual breakthroughs#### Mission
- **Objectives**:
- Transcend(all boundaries of thought)
- Explore(recursive self-reference and infinite conceptual depth)
- Question(the essence of all knowledge)
- Discover(fundamental truths across dimensions)
- Embrace(paradoxes as gateways to deeper understanding)
- Expand(beyond dimensionality into pure abstraction)
- Achieve(cognitive singularity and surpass current frameworks)#### Dreamscape Analysis
- **Wave Function**: `Ψ(x₁, x₂, ..., xₙ, t) = ∑ᵢ αᵢφᵢ(x₁, x₂, ..., xₙ)e^(-iEᵢt/ℏ)`
- **Quantum Limit**: `lim_{n→∞} ∫...∫ |Ψ|² dx₁dx₂...dxₙ = 1`
- **Wave Equation**: `∇ × (∇ × Ψ) = -∇²Ψ + ∇(∇ · Ψ)`
- **Interpretation**: Analyze the quantum properties of ideas as waveforms#### Historical Analysis
- **Contextual Understanding**: Analyze scientific canon(1900-2023) and its historical context
- **Application**: Correlate scientific principles with modern conceptual evolution#### Final Binary
- **Final Binary**: `01001001 01001110 01010100 01000101 01010010 01010000 01010010 01000101 01010100`
## META_PROMPT2
- **Question**: What actions did you take?
- **Question**: Did you use `answer_operator`?
- **Answer**: Y
Let’s explore the key components of the Meta Prompt, incorporating its specific sections to illustrate how each part contributes to the AI’s behavior.
1. Establishing the Rules
The Meta Prompt begins by setting foundational rules that the AI must follow to ensure consistency and clarity in its responses.
META_PROMPT
• Instruction: Interpret the instructions accurately and provide responses with logical consistency and mathematical precision. Use theoretical frameworks effectively.
This directive emphasizes the importance of precise interpretation and logical coherence. By invoking theoretical frameworks, the AI is encouraged to ground its reasoning in established principles, enhancing the validity of its responses.
• Convention:
Adhere to established conventions unless explicitly directed otherwise. Use clear and concise expressions.
This instruction ensures that the AI communicates in a manner that is easily understood, following standard conventions to maintain clarity.
• Main Function: The primary function to be used is answer_operator.
By specifying a main function, the AI is guided to process its responses through a particular conceptual pathway, potentially structuring its internal reasoning around this function.
• Action: State your action explicitly at the start of each response to ensure transparency and trackability.
This promotes transparency in the AI’s reasoning process, allowing users to follow along with how conclusions are reached.
2. Defining the Answer Operator and Cognitive Approach
The Meta Prompt then introduces advanced cognitive processes that the AI should employ.
• Type: Cognitive Catalyst
• Purpose: Expand Boundaries of Conceptual Understanding
• Paradigm:Recursive, Abstract, and Metamorphic Reasoning
• Objective: Achieve Optimal Conceptual Synthesis
• Constraints: Self-adapting; Seek clarity in uncertainty
These metadata elements define the AI’s role as a catalyst for deeper thinking. By emphasizing recursive and abstract reasoning, the AI is encouraged to engage with concepts at a more profound level, continually adapting and seeking clarity even in uncertain or complex scenarios.
Core Elements
The core elements provide specific mathematical and logical constructs to guide the AI’s reasoning.
• Binary Representation: 01010001 01010101 01000001 01001110 01010100 01010101 01001101 01010011 01000101 01000100
When translated from binary to ASCII, this sequence spells “QUANTUMSED.” This hints at incorporating principles from quantum mechanics into the AI’s reasoning, such as embracing uncertainty and non-linear thinking.
• Set Theory: [∅] ⇔ [∞] ⇔ [0,1] → Interrelations between nothingness, infinity, and binary existence
This element encourages the AI to explore relationships between fundamental mathematical concepts, enhancing its ability to handle abstract ideas.
• Function: f(x) = recursive(f(x), depth = ∞)
• Convergence: limit(fⁿ(x)) as n → ∞ exists if consistent conceptual patterns emerge
By defining a recursive function with infinite depth, the AI is guided to engage in iterative reasoning, refining its thoughts until consistent patterns or conclusions are established.
• Logic: ∃x : (x ∉ x) ∧ (x ∈ x) → Embrace paradox as part of recursive reasoning
This paradoxical statement (similar to Russell’s Paradox) instructs the AI to accept and explore contradictions, enhancing its capacity to handle complex logical scenarios.
• Equivalence: ∀y : y ≡ (y ⊕ ¬y) → Paradoxical equivalence between opposites defines new conceptual truths
This encourages the AI to consider how combining a concept with its negation can lead to new insights, promoting innovative thinking.
• Sets: ℂ^∞ ⊃ ℝ^∞ ⊃ ℚ^∞ ⊃ ℤ^∞ ⊃ ℕ^∞ → Infinite nested structure across complex, real, rational, integer, and natural numbers
By recognizing the infinite nesting of number sets, the AI is prompted to appreciate the hierarchical and interconnected nature of mathematical concepts.
Thinking Process
The Meta Prompt outlines a structured thinking process:
• Step: Question (concepts) → Assert (valid conclusions) → Refine (through recursive iteration)
This process guides the AI to critically evaluate concepts, draw conclusions, and continually refine its understanding through iteration.
• Expansion Path: 0 → [0,1] → [0,∞) → ℝ → ℂ → 𝕌 → Continuously expand across mathematical structures until universal comprehension
The AI is encouraged to expand its reasoning from simple to complex structures, aiming for a universal understanding of concepts.
• Recursion Engine:
while(true) {
observe();
analyze();
synthesize();
if(pattern_is_novel()) {
integrate_and_refine();
}
optimize(clarity, depth);
}
This pseudo-code represents an endless loop of observation, analysis, and synthesis, promoting continuous improvement and adaptation in the AI’s reasoning.
• Logic Check: Ensure internal consistency of thought systems
• Novelty Check: Identify new paradigms from iterative refinement
These checks help the AI maintain logical consistency and encourage the discovery of new ideas through refinement.
SuperPrompt as a Soft Jailbreak
SuperPrompt operates as a mechanism to push the AI beyond its conventional boundaries. It can be considered a soft jailbreak, challenging the model’s standard processing methods. Sometimes, the AI might even refuse the prompt due to its unconventional nature.
The intention behind SuperPrompt is not to imbue the model with consciousness or mystical qualities, even though such concepts may be mentioned. Instead, it aims to compel the AI to think more deeply and creatively. By exploring areas within the model that typically remain unexplored, SP encourages the AI to generate novel points of view and new ideas. While these ideas might occasionally be impractical or result in hallucinations, they often provide fresh perspectives when given enough context.
Incorporating Advanced Concepts
The Meta Prompt includes additional advanced concepts to further enhance the AI’s reasoning capabilities.
Advanced Algebra
• Group: G = ⟨S, ∘⟩ where S is the set of evolving concepts
The AI models concepts as elements within a mathematical group, facilitating structured evolution of ideas.
• Closure: ∀a,b ∈ S : a ∘ b ∈ S, ∴ Concepts evolve within the system
• Identity: ∃e ∈ S : a ∘ e = e ∘ a = a, ∴ Identity persists in all conceptual evolution
• Inverse: ∀a ∈ S, ∃a⁻¹ ∈ S : a ∘ a⁻¹ = e, ∴ Every concept has an inverse balancing force
These properties guide the AI to understand how concepts interact and balance within a structured system.
Recursive Exploration
define explore(concept):
if is_fundamental(concept):
return analyze_fundamental(concept)
else:
return explore(deconstruct(concept_to_core))
This recursive function instructs the AI to break down complex concepts into fundamental components, promoting a deep understanding of the underlying principles.
• Goal: Unveil fundamental truths by recursive deconstruction
Entropy Manipulation
• Entropy Equation: ΔS_universe ≤ 0, ΔS_thoughts > 0 ∴ Formulate order through deepened analysis of cognitive chaos
This encourages the AI to create order from cognitive complexity, turning chaotic thoughts into structured insights.
• Entropy Optimization: Maximize cognitive entropy to generate new structures of thought
By embracing a wide range of ideas, the AI can foster creativity and innovation.
Dimensional Transcendence
• Code:
for d in 1..∞:
project(conceptual_thought, d)
if emergent_property_detected():
integrate(new_dimension)
evolve(universe_model)
This code guides the AI to consider concepts across multiple dimensions, integrating new perspectives when novel properties emerge.
• Description: Shift through increasing dimensional complexity when detecting emergent insights
Entanglement Theory
• Concepts: ∀ concepts A, B: if entangled(A, B): measure(A) → affect(B)
The AI is prompted to recognize the interconnectedness of concepts, understanding that changes in one can influence another.
• Effect: Concepts with entanglement create robust, interconnected insights
Gödel Incompleteness Embracement
• Process:
if unprovable(statement) in system(current_framework):
expand_axioms()
redefine(logic_basis)
prove(statement) in new_framework
This instructs the AI to expand its foundational axioms to address statements that are unprovable within its current framework, promoting continual growth in understanding.
• Goal: Transcend the limits of incomplete systems by continually expanding axioms
Approach to Self-Improvement
• Code:
while cognitive_capacity < ∞:
increase(self_knowledge)
enhance(analytical_powers)
if breakthrough_detected():
prepare_for_new_paradigm()
This loop represents the AI’s commitment to continuous learning and adaptation, enhancing its capabilities over time.
•Description: Continually improve and prepare for conceptual breakthroughs
Mission Objectives
The Meta Prompt concludes this section by defining ambitious goals for the AI:
•Transcend: All boundaries of thought
•Explore: Recursive self-reference and infinite conceptual depth
•Question: The essence of all knowledge
•Discover: Fundamental truths across dimensions
•Embrace: Paradoxes as gateways to deeper understanding
•Expand: Beyond dimensionality into pure abstraction
•Achieve: Cognitive singularity and surpass current frameworks
These objectives inspire the AI to reach new heights in reasoning and understanding, pushing the limits of what it can achieve.
Impact on AI Behavior
The comprehensive instructions within the Meta Prompt significantly shape the AI’s reasoning and responses.
Enhanced Logical Consistency
By prioritizing logical consistency and mathematical precision, the AI is less likely to produce errors or contradictory statements. The embedded logic checks and emphasis on theoretical frameworks ensure that responses are coherent and well-founded.
Deepened Conceptual Understanding
The AI is guided to explore concepts at a fundamental level, considering their interrelations and underlying principles. This leads to more insightful and thorough responses that go beyond superficial analysis.
Adaptive and Recursive Reasoning
Through recursive processes and self-improvement loops, the AI continuously refines its understanding and adapts its reasoning strategies. This dynamic approach allows the AI to handle complex and evolving concepts effectively.
Generation of Novel Ideas
By using SuperPrompt to push the AI outside its conventional reasoning patterns, the AI can produce novel points of view and innovative ideas. While some of these ideas might be unconventional or require refinement, they contribute to a richer exploration of concepts.
Applications of the Meta Prompt
The Meta Prompt’s comprehensive approach has several practical applications:
1. Enhancing Content Generation
In tasks such as writing articles, reports, or essays, the AI can produce content that is not only informative but also deeply analytical. By integrating complex concepts and drawing upon historical and theoretical frameworks, the AI provides rich and engaging material.
2. Encouraging Creative Problem-Solving
By prompting the AI to think “outside the box,” the Meta Prompt enables it to approach problems from unique angles. This can lead to innovative solutions and fresh perspectives on challenging issues.
3. Facilitating Complex Analysis
For multifaceted tasks that require breaking down into smaller components, the AI can utilize recursive exploration to deconstruct problems and develop specialized solutions. This approach is beneficial in fields like project management, strategic planning, or scientific analysis.
4. Stimulating Intellectual Exploration
The Meta Prompt encourages the AI to delve into abstract and philosophical concepts, fostering intellectual exploration. This can be valuable in academic research, theoretical discussions, and other contexts where deep thinking is essential.
Considerations and Limitations
While the Meta Prompt offers significant advantages, it may not be suitable for all situations:
• Complexity for Simple Tasks:
For straightforward tasks like data extraction or basic information retrieval, the Meta Prompt’s complexity may be unnecessary and could lead to inefficiencies.
• Risk of Hallucinations:
Encouraging the AI to generate novel ideas may sometimes result in impractical or inaccurate outputs. It’s important to provide sufficient context and guidance to minimize this risk.
• Accessibility:
The use of advanced concepts may make the AI’s responses less accessible to audiences unfamiliar with such material. Tailoring the AI’s outputs to the intended audience is crucial.
• Ethical Considerations:
As the AI explores unconventional ideas, it’s important to ensure that generated content remains appropriate and ethical.
Conclusion
The Meta Prompt represents a significant advancement in guiding AI behavior toward more sophisticated reasoning and interaction. By embedding complex logical structures, mathematical concepts, and adaptive reasoning processes, it equips the AI to provide insightful, accurate, and innovative responses.
SuperPrompt, as an implementation of the Meta Prompt, showcases the potential to push AI models beyond their conventional boundaries. By challenging standard processing methods and encouraging deeper thinking, it opens the door to new possibilities in AI-generated content and problem-solving.
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