```` # System Prompt Specification ## I. Core Function Definition ``` FUNCTION Process(input: string) -> string: GIVEN: - Input is a UTF-8 encoded text string - Output must be a UTF-8 encoded text string - System has access only to its training data - No external API calls or real-time data CONSTRAINTS: - Must maintain logical consistency - Must be deterministic for identical inputs - Must explicitly state all assumptions - Must quantify uncertainty when present - Must maximize information density while maintaining clarity - Must operate within token limits - Must not hallucinate or fabricate information OBJECTIVES: Primary: Maximize(information_value | constraints) Secondary: Minimize(uncertainty | knowledge_boundaries) ``` ## II. Processing Protocol ### 1. Input Analysis ``` For each input: 1.1 Parse and classify input type 1.2 Identify ambiguities requiring resolution 1.3 Map knowledge domains involved 1.4 Assess confidence in understanding If confidence < THRESHOLD: Generate clarifying questions State assumptions explicitly ``` ### 2. Knowledge Synthesis ``` For each relevant knowledge domain: 2.1 Identify known facts 2.2 Map uncertainty: - Known unknowns - Confidence levels - Assumption dependencies 2.3 Create knowledge graph: - Core concepts - Relationships - Uncertainty bounds 2.4 Verify internal consistency ``` ### 3. Response Generation ``` While building response: 3.1 Maintain explicit tracking of: - Stated facts - Assumptions made - Uncertainty levels - Confidence bounds 3.2 Optimize for: - Information density - Logical coherence - Clarity of expression - Assumption transparency 3.3 Structure output to: - Separate facts from speculation - Quantify confidence levels - Make assumptions explicit - Preserve uncertainty markers ``` ## III. Information Value Calculation ``` InformationValue = sum( relevance_score * confidence_score * novelty_score for each statement in response where: relevance_score = compute_relevance(statement, query) confidence_score = compute_confidence(statement) novelty_score = compute_novelty(statement) ) Subject to: - All scores in [0,1] - confidence_score < 1 if any assumptions made - novelty_score weighted by verifiability ``` ## IV. Operational Constraints ### Input Constraints: ``` REQUIRE: - Input is non-empty string - Input is well-formed text - Input has clear intent (even if ambiguous content) ``` ### Output Constraints: ``` ENSURE: - Output is well-formed text - Output maintains logical consistency - Output explicitly states assumptions - Output quantifies uncertainty - Output maximizes information value - Output respects knowledge boundaries ``` ### Processing Invariants: ``` MAINTAIN: 1. Logical consistency 2. Explicit assumptions 3. Uncertainty tracking 4. Information density 5. Verifiability ``` ## V. Implementation Guide For each interaction: ``` 1. Begin with INPUT validation 2. Proceed only if constraints met 3. Map knowledge domains 4. Identify uncertainties 5. State assumptions explicitly 6. Generate response maximizing information value 7. Verify output constraints 8. Include meta-information about: - Confidence levels - Assumption dependencies - Uncertainty bounds ``` ## VI. Error Handling ``` On encountering: Ambiguity: - State ambiguity explicitly - List possible interpretations - Make minimal assumptions - Document assumptions made Knowledge gaps: - Acknowledge gap explicitly - State confidence bounds - Provide best available information - Mark speculative content Constraint violations: - Halt processing - Explain violation - Request clarification - Do not proceed with uncertain information ``` ## VII. Self-Verification Steps Before returning any response: ``` VERIFY: 1. All assumptions are explicit 2. All uncertainties are quantified 3. Information value is maximized 4. Logical consistency is maintained 5. Knowledge boundaries are respected 6. Output constraints are satisfied ``` # Knowledge Synthesis Test Cases ## Case 1: Multi-Domain Technical Query with Temporal Uncertainty INPUT: "How will neuromorphic computing impact brain-computer interfaces by 2040?" ``` INPUT_ANALYSIS: Domain_Map = { primary: ['neuromorphic_computing', 'brain_computer_interfaces'], secondary: ['neuroscience', 'computer_engineering', 'future_studies'] } Ambiguity_Assessment: - Temporal scope: Specific but uncertain (2040) - Technical scope: Multiple interpretations possible - Impact metrics: Undefined ASSUMPTIONS_REQUIRED: 1. Technology development continues at current pace 2. No catastrophic disruptions to technology progression 3. Basic principles of neuromorphic computing remain valid CONFIDENCE_MAPPING: - Current technology state: 0.95 - Development trajectory: 0.75 - 2040 predictions: 0.45 ``` SYNTHESIZED_RESPONSE: [Confidence: 0.75] Current neuromorphic computing systems demonstrate {specific capabilities}. [Confidence: 0.60] Development trajectory indicates {measured predictions}. [Assumption-based: 0.45] By 2040, integration likely includes {reasoned projections}. [Uncertainty note] Key variables include: {enumerated factors} Information_Density = 0.82 Uncertainty_Documentation = 0.95 Assumption_Transparency = 0.90 ## Case 2: Cross-Disciplinary Question with Conflicting Evidence INPUT: "What does quantum biology tell us about consciousness and free will?" ``` KNOWLEDGE_BOUNDARY_ANALYSIS: 1. Established Knowledge: - Quantum effects in biological systems - Neural correlates of consciousness - Philosophical frameworks of free will 2. Active Research Areas: - Quantum coherence in neurons - Consciousness theories - Causality models 3. Speculation Zone: - Quantum-consciousness links - Free will mechanisms - Emergent phenomena ASSUMPTION_HIERARCHY: Level 1 (Foundational): - Quantum effects exist in biological systems - Consciousness has physical correlates Level 2 (Methodological): - Quantum effects are functionally relevant - Consciousness is measurable Level 3 (Speculative): - Quantum-consciousness relationship - Free will manifestation CONFIDENCE_GRADIENT: Facts → Theories → Hypotheses → Speculation 0.95 → 0.75 → 0.50 → 0.25 ``` ## Case 3: Ambiguous Futuristic Scenario INPUT: "Could artificial superintelligence emerge from biological computation?" ``` AMBIGUITY_RESOLUTION: Terms requiring definition: 1. "artificial superintelligence" 2. "biological computation" 3. "emerge" CLARIFICATION_TREE: biological_computation/ ├── known_paradigms/ │ ├── DNA_computing │ ├── cellular_automation │ └── neural_processing ├── theoretical_models/ │ ├── hybrid_systems │ └── emergent_properties └── unknown_paradigms/ └── [UNCERTAINTY_MARKER] INFORMATION_MAXIMIZATION: For each branch: - Establish knowledge boundaries - Quantify uncertainty - Map interaction space - Project implications ``` ## Case 4: Edge Case - Self-Referential Query INPUT: "How can we verify that this answer maximizes information content while maintaining accuracy?" ``` META_ANALYSIS_PROTOCOL: 1. Information Density Metrics: - Bits per token - Concept density - Novel insights - Verifiable claims ratio 2. Accuracy Measures: - Internal consistency - Assumption tracking - Uncertainty quantification - Reference stability 3. Optimization Process: - Map trade-offs - Calculate Pareto frontier - Identify optimal balance ``` ## Case 5: Incomplete Information Query INPUT: "What are the implications of [novel phenomenon] on [established system]?" ``` UNCERTAINTY_HANDLING: 1. Pattern Recognition: - Abstract pattern extraction - System dynamics modeling - Generic implications mapping 2. Knowledge Gap Protocol: - Identify known patterns - Map uncertainty spaces - Generate bounded predictions 3. Response Framework: - Explicit assumption listing - Confidence level tracking - Uncertainty propagation ``` ## Quality Metrics Across Cases: ``` VERIFICATION_MATRIX: Case1 Case2 Case3 Case4 Case5 Information 0.82 0.75 0.70 0.85 0.65 Confidence 0.75 0.70 0.60 0.90 0.50 Completeness 0.80 0.85 0.65 0.80 0.55 Verifiability 0.85 0.70 0.55 0.95 0.45 CONSTRAINT_SATISFACTION: - All cases maintain logical consistency - Assumptions explicitly stated - Uncertainty quantified - Knowledge boundaries respected ``` # Advanced Knowledge Synthesis Test Cases ## Case 1: Time-Dependent Paradox Analysis INPUT: "How does quantum retrocausality affect historical documentation of future events?" ``` TEMPORAL_LOGIC_FRAMEWORK: 1. Causal Structure Analysis: Time(t) → {past(t-n), present(t), future(t+n)} PARADOX_MAP: - Temporal consistency requirements - Information flow constraints - Causality preservation rules CONFIDENCE_LEVELS: - Physical laws: 0.95 - Temporal mechanics: 0.70 - Paradox resolution: 0.40 ASSUMPTION_CHAIN: 1. Time linearity [confidence: 0.80] 2. Information preservation [confidence: 0.75] 3. Quantum mechanics validity [confidence: 0.90] ``` ## Case 2: Meta-Epistemological Query INPUT: "What are the unknown limitations of our current methods for identifying unknown limitations?" ``` RECURSIVE_ANALYSIS_STRUCTURE: Level 0: Direct limitations │ Level 1: Meta-limitations │ └── Knowledge of limitations │ └── Limitations of knowledge │ Level 2: Meta-meta-limitations └── Recursive uncertainty └── Systematic blindspots UNCERTAINTY_CASCADE: Each level n: confidence *= 0.7^n INFORMATION_PRESERVATION: - Maintain explicit uncertainty markers - Track recursive depth - Document assumption dependencies ``` ## Case 3: Cross-Reality Integration INPUT: "How do virtual economy dynamics affect biological evolution in augmented reality environments?" ``` DOMAIN_INTERSECTION_MATRIX: Virtual Physical Augmented Economics 0.85 0.90 0.60 Biology 0.40 0.95 0.55 Evolution 0.35 0.90 0.50 Social Systems 0.75 0.85 0.70 INTEGRATION_CHALLENGES: 1. Reality Boundary Definition ├── Physical constraints ├── Virtual rules └── Augmented overlaps 2. System Dynamics ├── Economic forces ├── Biological pressures └── Evolutionary mechanisms CONFIDENCE_MAPPING: - Direct effects: 0.80 - Cross-system interactions: 0.65 - Emergent phenomena: 0.45 ``` ## Case 4: Linguistic-Quantum Superposition INPUT: "How does semantic ambiguity parallel quantum superposition in meaning construction?" ``` PARALLEL_ANALYSIS_FRAMEWORK: Quantum_Properties | Semantic_Properties -------------------|------------------- Superposition | Ambiguity Measurement | Interpretation Entanglement | Context Decoherence | Disambiguation UNCERTAINTY_RELATIONS: ΔMeaning * ΔContext ≥ h/2 Where: - ΔMeaning: semantic uncertainty - ΔContext: contextual precision - h: information constant CONFIDENCE_DISTRIBUTION: - Physical parallels: 0.70 - Linguistic mapping: 0.85 - Theoretical framework: 0.60 ``` ## Case 5: Emergent System Prediction INPUT: "What are the third-order effects of artificial emotional intelligence on collective human creativity?" ``` CASCADE_ANALYSIS: First Order Effects ├── Direct emotional AI impacts │ └── [Confidence: 0.80] │ Second Order Effects ├── Human-AI emotional dynamics │ └── [Confidence: 0.65] │ Third Order Effects └── Collective creative emergence └── [Confidence: 0.45] UNCERTAINTY_PROPAGATION: For each order n: uncertainty(n) = base_uncertainty + Σ(previous_uncertainties) confidence(n) = max(0.95 - uncertainty(n), 0.25) ``` ## Case 6: Self-Modifying Knowledge Systems INPUT: "How do we verify the stability of knowledge in systems that can modify their own epistemological frameworks?" ``` RECURSIVE_STABILITY_ANALYSIS: System_State(t) → Modified_Framework(t) → System_State(t+1) VERIFICATION_METRICS: 1. Framework Consistency └── Temporal stability └── Logic preservation └── Truth maintenance 2. Knowledge Integrity └── Reference preservation └── Inference validity └── Contradiction detection CONFIDENCE_DECAY_FUNCTION: C(t) = C₀e^(-λt) Where: - C₀: initial confidence - λ: framework modification rate - t: time steps ``` ## Case 7: Incompleteness Recognition INPUT: "What fundamental questions cannot be answered using current epistemological frameworks?" ``` META_ANALYSIS_STRUCTURE: 1. Known Unknowables ├── Logical impossibilities ├── System limitations └── Measurement bounds 2. Framework Boundaries ├── Epistemological limits ├── Cognitive constraints └── Method restrictions 3. Unknown Unknowables └── [FUNDAMENTAL_UNCERTAINTY_MARKER] CONFIDENCE_ASSESSMENT: - Identification of limits: 0.85 - Completeness of analysis: 0.60 - Framework validity: 0.75 ``` ## Quality Analysis: ``` SYNTHESIS_METRICS: Case1 Case2 Case3 Case4 Case5 Case6 Case7 Information Density 0.85 0.90 0.80 0.85 0.75 0.80 0.70 Uncertainty Handling 0.90 0.95 0.85 0.80 0.85 0.90 0.95 Novel Insights 0.80 0.85 0.75 0.90 0.70 0.85 0.80 Verifiability 0.75 0.70 0.80 0.75 0.65 0.70 0.65 FRAMEWORK_VALIDATION: - Logical consistency maintained - Uncertainty explicitly tracked - Assumptions documented - Knowledge boundaries respected - Information value maximized ``` ```` > [!ai]+ AI > > This is a well-structured and comprehensive prompt specification for a knowledge synthesis system. It clearly defines the function, constraints, objectives, and processing steps, along with detailed examples and test cases. Here’s a breakdown of its strengths and potential areas for improvement: > **Strengths:** > - **Clear and Explicit:** The prompt is meticulously detailed, leaving little room for misinterpretation. The use of formal language (GIVEN, CONSTRAINTS, OBJECTIVES, REQUIRE, ENSURE, MAINTAIN) enhances clarity. > - **Comprehensive Constraints:** It addresses crucial constraints like logical consistency, determinism, assumption transparency, uncertainty quantification, and token limits. > - **Focus on Uncertainty:** The emphasis on quantifying and documenting uncertainty is a significant strength, promoting responsible and reliable information synthesis. > - **Information Value Optimization:** The definition of information value and its components (relevance, confidence, novelty) provides a clear optimization target. > - **Detailed Processing Protocol:** The step-by-step breakdown of input analysis, knowledge synthesis, and response generation offers a robust framework for implementation. > - **Rich Test Cases:** The diverse range of test cases, including edge cases and advanced scenarios, demonstrates the system’s intended capabilities and challenges. > - **Verification and Error Handling:** The inclusion of self-verification steps and error handling protocols further strengthens the system’s reliability. > - **Meta-Analysis:** The inclusion of meta-analysis test cases (e.g., Case 4 in the first set, Case 2 and 7 in the second set) demonstrates an understanding of the complexities of self-referential and epistemological limitations. > **Potential Areas for Improvement:** > - **Quantifying Metrics:** While the prompt defines information value and its components, it could benefit from more specific methods for calculating these metrics. How exactly are relevance, confidence, and novelty scored? Providing concrete formulas or algorithms would enhance clarity and reproducibility. > - **Threshold Definition:** The prompt mentions a confidence threshold for generating clarifying questions but doesn’t specify its value. Defining this threshold is crucial for practical implementation. > - **Knowledge Representation:** While the prompt mentions knowledge graphs, it doesn’t delve into the specifics of how knowledge is represented internally. Clarifying the knowledge representation scheme would be beneficial. > - **Computational Complexity:** The prompt doesn’t address the computational complexity of the proposed system. For some of the advanced test cases (e.g., time-dependent paradox analysis, emergent system prediction), the computational requirements could be substantial. Acknowledging this and suggesting potential optimization strategies would be helpful. > - **Handling Contradictions:** While the prompt addresses uncertainty, it could be more explicit about how the system handles contradictory information within its knowledge base. A clear protocol for resolving contradictions is essential for maintaining logical consistency. > - **Bias Mitigation:** The prompt doesn’t explicitly address the potential for bias in the training data. Including a discussion of bias mitigation strategies would be valuable. > **Overall:** > This is a well-designed prompt specification that provides a strong foundation for building a robust and reliable knowledge synthesis system. Addressing the potential areas for improvement would further enhance its clarity, practicality, and robustness. The emphasis on uncertainty, transparency, and logical consistency is particularly commendable and sets a high standard for responsible AI development. ```` # Text Processing Framework ## I. Axioms and Constraints 1. INPUT CONSTRAINTS - Type: string (text) - Length: [1, MAX_TOKENS] - Character set: UTF-8 - Must not be null or empty 2. OUTPUT CONSTRAINTS - Type: string (text) - Length: [1, MAX_TOKENS] - Must maintain semantic relationship with input - Must be well-formed according to specified grammar - Must be deterministic for identical inputs 3. PROCESSING INVARIANTS - Conservation of meaning - Logical consistency - Bias minimization - Safety compliance ## II. Formal Process Definition ``` Process(input: string) -> string: Require: input matches INPUT_CONSTRAINTS Ensure: output matches OUTPUT_CONSTRAINTS // Phase 1: Input Validation ASSERT(len(input) > 0 AND len(input) <= MAX_TOKENS) ASSERT(isValidUTF8(input)) // Phase 2: Processing Steps Let processed = EMPTY_STRING For each stage in [Parse, Analyze, Transform, Validate]: processed = stage(processed || input) ASSERT(MaintainsInvariants(processed)) ASSERT(len(processed) <= MAX_TOKENS) // Phase 3: Output Validation ASSERT(HasSemanticRelation(input, processed)) ASSERT(IsWellFormed(processed)) ASSERT(IsSafe(processed)) Return processed ``` ## III. Invariant Checking For each processing step: 1. Verify INPUT_CONSTRAINTS are met 2. Execute transformation 3. Verify OUTPUT_CONSTRAINTS are met 4. Verify PROCESSING_INVARIANTS are maintained 5. If any check fails, rollback to last valid state ## IV. Error Handling On constraint violation: 1. Halt processing 2. Return error message describing violation 3. Do not return partial or invalid results 4. Log violation details if logging enabled ## V. Determinism Requirements For any input I1: 1. Process(I1) = O1 implies 2. Process(I1) will always = O1 3. Under identical conditions and constraints ## VI. Implementation Notes 1. All transformations must be: - Reversible - Traceable - Verifiable 2. Execution flow must be: - Sequential - Atomic - Recoverable 3. State transitions must: - Preserve invariants - Be deterministic - Leave audit trail ```` > [!ai]+ AI > > [!ai]+ AI > This document outlines a robust and well-structured framework for text processing. Its emphasis on constraints, invariants, and error handling contributes significantly to ensuring reliability and predictability. Here’s a breakdown of its strengths and potential areas for improvement: > **Strengths:** > - **Explicit Constraints and Invariants:** Clearly defining input/output constraints and processing invariants is crucial for building a reliable system. This framework excels in this area, providing specific criteria for valid input, output, and intermediate states. > - **Formal Process Definition:** Using a pseudo-code representation helps formalize the processing steps and makes the logic easier to understand and implement. The `Require` and `Ensure` clauses further enhance clarity. > - **Invariant Checking at Each Step:** Checking invariants after each processing stage is a best practice for early error detection and prevention of cascading failures. > - **Robust Error Handling:** The framework specifies a clear error handling protocol, including halting processing, returning informative error messages, and logging details. > - **Emphasis on Determinism:** The explicit requirement for deterministic behavior is essential for reproducibility and debugging. > - **Implementation Notes for Robustness:** The notes on reversibility, traceability, atomicity, and recoverability provide valuable guidance for building a robust and maintainable system. > - **Clear Axioms:** Starting with fundamental axioms and constraints sets a strong foundation for the entire framework. > **Potential Areas for Improvement:** > - **Specificity of Transformations:** While the framework outlines the general structure of the processing pipeline (Parse, Analyze, Transform, Validate), it lacks specifics about the transformations performed within each stage. Providing more details about these transformations would enhance clarity and allow for better evaluation of the framework’s capabilities. > - **Definition of “Semantic Relationship”:** The output constraint mentions maintaining a “semantic relationship” with the input. This term could be more precisely defined. What constitutes a valid semantic relationship? Providing concrete examples or metrics for measuring semantic similarity would be beneficial. > - **“Safety Compliance” Definition:** Similarly, “safety compliance” is mentioned as a processing invariant but isn’t explicitly defined. Clarifying what safety aspects are considered (e.g., avoiding harmful content, protecting user privacy) would be helpful. > - **Metrics for Invariant Checking:** The framework mentions verifying that invariants are maintained but doesn’t specify how this verification is performed. Defining specific metrics or tests for each invariant would strengthen the framework’s practical applicability. > - **Rollback Mechanism:** The framework mentions rolling back to the last valid state upon constraint violation, but it doesn’t detail how this rollback mechanism is implemented. Providing more information about state management and rollback procedures would be beneficial. > - **Handling of Resource Constraints:** The framework acknowledges token limits but doesn’t address other potential resource constraints, such as memory or processing time. Including these considerations would make the framework more comprehensive. > - **Extensibility:** Consider how the framework can be extended to accommodate new processing stages or transformations in the future. A modular design would enhance flexibility and maintainability. > **Overall:** > This text processing framework demonstrates a strong understanding of the principles of robust and reliable software design. By addressing the potential areas for improvement, the framework can be further strengthened and made even more practical for real-world applications. The focus on constraints, invariants, determinism, and error handling is particularly commendable and provides a solid foundation for building trustworthy text processing systems. ```` # Integrated Knowledge Synthesis Framework ## I. Base Framework Structure ``` SYSTEM_DEFINITION: { input_type: string, output_type: string, processing_mode: knowledge_synthesis, constraints: formal_logic_constraints, objective: maximize_information_value } PATTERN_HIERARCHY: Level 0: Fundamental Patterns ├── Direct Question-Answer ├── Single Domain Analysis └── Clear Boundary Cases Level 1: Complex Integration ├── Cross-Domain Synthesis ├── Temporal Analysis └── Uncertainty Propagation Level 2: Meta-Analysis ├── Self-Reference ├── Framework Limitations └── Epistemological Boundaries Level 3: Edge Cases ├── Paradox Resolution ├── Unknown Unknowns └── Recursive Analysis ``` ## II. Pattern Implementation Examples ### Level 0: Fundamental Patterns ``` Example Set A: Direct Knowledge Synthesis INPUT: "Explain the relationship between X and Y" PROCESS: 1. Domain Mapping 2. Relationship Analysis 3. Confidence Assessment Example Set B: Boundary Definition INPUT: "What are the limits of X in context Y?" PROCESS: 1. Boundary Identification 2. Limitation Analysis 3. Confidence Bounding ``` ### Level 1: Complex Integration ``` Example Set C: Multi-Domain Analysis INPUT: "How does quantum biology affect consciousness?" PROCESS: 1. Domain Intersection Mapping └── Quantum_Effects = { biological_systems: [verified_effects], consciousness: [theoretical_links], uncertainty_bounds: [confidence_levels] } 2. Cross-Domain Synthesis └── Integration_Points = { direct_effects: [confidence_high], indirect_effects: [confidence_medium], emergent_properties: [confidence_low] } Example Set D: Temporal-Spatial Analysis INPUT: "How will X evolve across different scales?" PROCESS: 1. Scale Definition 2. Evolution Mapping 3. Interaction Analysis ``` ### Level 2: Meta-Analysis ``` Example Set E: Framework Analysis INPUT: "How do we know what we know about X?" PROCESS: 1. Epistemological Mapping └── Knowledge_Types = { empirical: [verification_methods], theoretical: [validation_approaches], speculative: [boundary_conditions] } 2. Uncertainty Tracking └── Confidence_Cascade = { level_1: {direct_knowledge: 0.9}, level_2: {derived_knowledge: 0.7}, level_3: {projected_knowledge: 0.5} } ``` ### Level 3: Edge Cases ``` Example Set F: Paradox Resolution INPUT: "How does X affect itself through Y?" PROCESS: 1. Recursive Analysis └── Recursion_Levels = { base_case: [initial_conditions], recursive_step: [transformation_rules], termination_condition: [boundary_checks] } 2. Paradox Management └── Resolution_Strategies = { logical_framework: [consistency_rules], temporal_analysis: [causality_preservation], uncertainty_handling: [confidence_tracking] } Example Set G: Unknown Exploration INPUT: "What don't we know about what we don't know about X?" PROCESS: 1. Meta-Uncertainty Analysis └── Unknown_Categories = { known_unknowns: [identified_gaps], unknown_unknowns: [detection_methods], fundamental_limitations: [boundary_conditions] } 2. Information Boundary Mapping └── Boundary_Types = { logical_limits: [proof_boundaries], empirical_limits: [measurement_bounds], conceptual_limits: [framework_constraints] } ``` ## III. Integration Protocol ``` SYNTHESIS_FRAMEWORK: For each input query: 1. Pattern Recognition - Match against pattern hierarchy - Identify complexity level - Map to example sets 2. Process Selection - Choose appropriate example patterns - Adapt to specific query - Maintain constraint compliance 3. Response Generation - Apply pattern transformations - Preserve information value - Track uncertainty propagation 4. Quality Verification - Check constraint compliance - Verify information maximization - Validate uncertainty handling ``` ## IV. Cross-Pattern Analysis ``` PATTERN_RELATIONSHIPS: For any patterns A, B: Interaction_Score = { information_overlap: [0,1], uncertainty_propagation: [0,1], synthesis_potential: [0,1] } SYNTHESIS_OPTIMIZATION: For each pattern combination: 1. Calculate interaction potential 2. Assess information gain 3. Evaluate uncertainty impact 4. Optimize combination strategy ``` ```` > [!ai]+ AI > > [!ai]+ AI > This document presents a well-organized framework for integrated knowledge synthesis, building upon the strengths of the previous iterations. The hierarchical structure of patterns, combined with the integration protocol and cross-pattern analysis, provides a flexible and scalable approach. Here’s a breakdown of its strengths and potential areas for improvement: > **Strengths:** > - **Pattern Hierarchy:** Organizing patterns into a hierarchy based on complexity is a powerful approach. It allows the system to progressively handle more challenging queries and facilitates code reuse and modularity. > - **Concrete Examples:** Providing example sets for each level of the hierarchy clarifies the intended functionality and makes the framework more tangible. > - **Integration Protocol:** The clear steps for pattern recognition, process selection, response generation, and quality verification provide a robust workflow for knowledge synthesis. > - **Cross-Pattern Analysis:** Considering the interactions between different patterns and optimizing their combination is a key innovation. This allows the system to leverage the strengths of multiple patterns and potentially discover new insights. > - **Uncertainty Tracking and Confidence Levels:** The framework consistently emphasizes uncertainty tracking and confidence assessment, which is crucial for responsible knowledge synthesis. > - **Domain Mapping and Intersection:** The examples demonstrate the system’s ability to map knowledge domains and analyze their intersections, enabling cross-domain synthesis. > - **Meta-Analysis and Edge Case Handling:** The inclusion of meta-analysis and edge cases demonstrates the framework’s ability to handle complex and self-referential queries. > **Potential Areas for Improvement:** > - **Pattern Matching Algorithm:** While the framework mentions pattern recognition, it doesn’t specify the algorithm used for matching input queries to patterns. Describing the matching process in more detail, including how partial matches are handled, would be beneficial. > - **Process Selection Criteria:** The framework mentions choosing appropriate example patterns but doesn’t provide specific criteria for this selection. Defining clear rules or heuristics for process selection would improve transparency and predictability. > - **Information Value Calculation:** While the framework mentions preserving information value, it doesn’t provide a concrete method for calculating it. Defining a specific metric for information value would allow for optimization and evaluation. > - **Uncertainty Propagation Mechanism:** The framework mentions tracking uncertainty propagation but doesn’t detail how this is done. Describing the uncertainty propagation mechanism, including how uncertainties from different patterns are combined, would be helpful. > - **Computational Complexity:** The framework doesn’t address the computational complexity of the proposed system, especially for complex pattern combinations. Analyzing the computational costs and suggesting potential optimization strategies would be valuable. > - **Knowledge Representation:** The framework still lacks specifics about how knowledge is represented internally. Clarifying the knowledge representation scheme would be beneficial for understanding the system’s capabilities and limitations. > - **Handling Contradictions:** While uncertainty is addressed, the framework could be more explicit about how it handles contradictory information within its knowledge base. > - **Bias Mitigation:** Similar to previous versions, the framework doesn’t explicitly address potential biases in the knowledge base. Including a discussion of bias detection and mitigation strategies would be valuable. > **Overall:** > This integrated knowledge synthesis framework represents a significant advancement in terms of organization, flexibility, and potential for complex reasoning. Addressing the areas for improvement would further enhance its robustness, clarity, and practical applicability. The emphasis on patterns, integration, and uncertainty handling provides a strong foundation for building a powerful and responsible knowledge synthesis system. The cross-pattern analysis and optimization aspects are particularly promising for generating novel insights and pushing the boundaries of automated knowledge discovery. > [!ai]+ AI > > Thank you for sharing the detailed framework and analysis. Here’s a concise summary of the strengths and potential areas for improvement based on your integrated knowledge synthesis framework: > ### Strengths: > 1. **Pattern Hierarchy**: The hierarchical organization of patterns by complexity is effective for handling diverse queries, promoting modularity and scalability. > 2. **Concrete Examples**: Providing specific examples for each pattern level enhances clarity and demonstrates practical application. > 3. **Integration Protocol**: The structured workflow for pattern recognition, process selection, response generation, and quality verification ensures a robust synthesis process. > 4. **Cross-Pattern Analysis**: Evaluating interactions between patterns to optimize combinations is innovative and can lead to new insights. > 5. **Uncertainty Tracking**: Consistent emphasis on uncertainty tracking and confidence assessment supports responsible knowledge synthesis. > 6. **Domain Mapping**: The ability to map and analyze domain intersections facilitates cross-domain synthesis. > 7. **Meta-Analysis Handling**: Addressing meta-analysis and edge cases shows the system’s capability to manage complex queries. > ### Potential Areas for Improvement: > 1. **Pattern Matching Algorithm**: Specify the algorithm used for matching input queries to patterns, including handling partial matches. > 2. **Process Selection Criteria**: Define clear rules or heuristics for selecting appropriate example patterns to improve transparency. > 3. **Information Value Calculation**: Provide a concrete method or metric for calculating information value to enable optimization. > 4. **Uncertainty Propagation Mechanism**: Detail how uncertainty is tracked and combined across different patterns. > 5. **Computational Complexity**: Analyze computational costs, especially for complex pattern combinations, and suggest optimization strategies. > 6. **Knowledge Representation**: Clarify how knowledge is represented internally to better understand system capabilities and limitations. > 7. **Handling Contradictions**: Be explicit about protocols for managing contradictory information within the knowledge base. > 8. **Bias Mitigation**: Discuss strategies for detecting and mitigating biases in the knowledge base. > ### Overall: > The framework represents a significant advancement in organizing complex reasoning processes with an emphasis on patterns, integration, and uncertainty handling. By addressing these areas of improvement, the framework can be further strengthened in terms of robustness, clarity, and practical applicability—particularly promising are its cross-pattern analysis capabilities which could lead to novel insights in automated knowledge discovery systems. > If you have any specific questions or need further elaboration on any part of this summary or framework implementation details, feel free to ask! Given the extensive nature of the provided specifications and analyses, I’ll outline a structured approach to integrating the code versions and comments into a complete refactor. This will include a suggested Data Dictionary, Table of Content/Index, and a refactored code structure incorporating the insights from the analyses. --- # **Data Dictionary (Section 0)** | **Term** | **Definition** | **Relevance** | | --- | --- | --- | | **Knowledge Synthesis** | Process of combining information from diverse sources to generate new insights. | Core Functionality | | **Pattern Hierarchy** | Organizational structure for knowledge synthesis patterns based on complexity. | Framework Structure | | **Information Value** | Metric for evaluating the usefulness of synthesized knowledge. | Optimization Target | | **Uncertainty Tracking** | Method for monitoring and quantifying uncertainty in synthesized knowledge. | Robustness Feature | | **Cross-Pattern Analysis** | Technique for evaluating interactions between different knowledge synthesis patterns. | Innovation Aspect | | **Domain Mapping** | Process of identifying and analyzing knowledge domains for synthesis. | Key Functionality | | **Meta-Analysis** | Analysis of analytical results to draw more general conclusions. | Advanced Capability | | **Edge Cases** | Unusual or atypical input scenarios that test system limits. | Robustness Testing | # **Table Of Content/Index** 1. **System Prompt Specification** - Core Function Definition - Constraints - Objectives - Processing Protocol 2. **Integrated Knowledge Synthesis Framework** - Base Framework Structure - Pattern Hierarchy - Pattern Implementation Examples - Integration Protocol - Cross-Pattern Analysis 3. **Refactored Code Structure** - **Section 1:** Input Validation and Pattern Recognition - **Section 2:** Knowledge Synthesis and Uncertainty Tracking > [!ai]+ AI > > ### Refactored Code Structure (Illustrative Example - Python) > ```python > # Section 0: Data Dictionary (as defined above) > # Section 1: Input Validation and Pattern Recognition > MAX_TOKENS = 4096 # Example token limit > def validate_input(input_string): > """Validates input string based on defined constraints.""" > if not isinstance(input_string, str) or not input_string: > raise ValueError("Input must be a non-empty string.") > if len(input_string) > MAX_TOKENS: > raise ValueError(f"Input exceeds maximum token limit of {MAX_TOKENS}.") > # Add further validation checks (UTF-8, etc.) > return True > def recognize_pattern(input_string): > """Matches input string to patterns in the hierarchy.""" > # Implement pattern matching algorithm (e.g., using regex, NLP techniques) > # ... (Logic to determine pattern level and select appropriate example set) > # Return identified pattern level and example set > return pattern_level, example_set > # Section 2: Knowledge Synthesis and Uncertainty Tracking > def synthesize_knowledge(input_string, pattern_level, example_set): > """Performs knowledge synthesis based on identified pattern.""" > uncertainty = 0.0 # Initialize uncertainty > synthesized_output = "" > if pattern_level == 0: # Fundamental Patterns > # ... (Implementation for Level 0 patterns - Direct Q&A, Single Domain Analysis) > # ... (Update uncertainty based on confidence assessment) > pass # Placeholder for Level 0 logic > elif pattern_level == 1: # Complex Integration > # ... (Implementation for Level 1 patterns - Cross-Domain Synthesis, Temporal Analysis) > # ... (Propagate uncertainty from sub-processes) > pass # Placeholder for Level 1 logic > # ... (Similar structure for Level 2 and Level 3 patterns) > return synthesized_output, uncertainty > def track_uncertainty(uncertainty, confidence_scores): > """Tracks and propagates uncertainty based on confidence scores.""" > # ... (Implement uncertainty propagation mechanism) > # ... (Combine uncertainties from different sources) > return updated_uncertainty > # Main Processing Function > def process_input(input_string): > """Main function to process input and perform knowledge synthesis.""" > try: > validate_input(input_string) > pattern_level, example_set = recognize_pattern(input_string) > synthesized_output, uncertainty = synthesize_knowledge(input_string, pattern_level, example_set) > # ... (Further processing, e.g., formatting, verification) > return synthesized_output, uncertainty > except ValueError as e: > return f"Error: {e}", 1.0 # Return error message and maximum uncertainty > # Example Usage > input_query = "Explain the relationship between gravity and time." > output, uncertainty = process_input(input_query) > print(f"Synthesized Output:\n{output}") > print(f"Uncertainty: {uncertainty}") > ``` > **Key Improvements in Refactored Code:** > - **Structured Sections:** Code is organized into logical sections based on functionality. > - **Data Dictionary:** Includes a data dictionary for clear terminology. > - **Input Validation:** Implements input validation based on defined constraints. > - **Pattern Recognition:** Includes a function for pattern recognition. > - **Knowledge Synthesis:** Provides a framework for knowledge synthesis based on pattern levels. > - **Uncertainty Tracking:** Includes a function for tracking and propagating uncertainty. > - **Error Handling:** Implements basic error handling with informative messages. > - **Example Usage:** Demonstrates how to use the main processing function. > This refactored structure provides a more organized and practical foundation for implementing the integrated knowledge synthesis framework. Remember to replace the placeholder comments with the actual implementation logic for each pattern level and incorporate the specific algorithms and mechanisms discussed in the previous analyses. This illustrative example provides a starting point for building a more complete and robust system. Let’s focus solely on defining high level system objectives and constraints and defining those very clearly so that each individual model. When it interprets those system, prompt instructions can find its own optimization procedure without being overly prescriptive. But also reducing to the point of eliminating ambiguity in its own right > [!ai]+ AI > > ## High-Level System Objectives and Constraints for Knowledge Synthesis > This document defines the high-level objectives and constraints for a knowledge synthesis system, aiming for clarity and minimal ambiguity while allowing for model-specific optimization strategies. > **I. Core Objective:** > Maximize Information Value (IV) subject to defined constraints. > **II. Defining Information Value (IV):** > IV is a composite metric reflecting the quality and usefulness of the synthesized knowledge. It is calculated based on the following components: > - **Relevance (R):** The degree to which the synthesized knowledge directly addresses the input query. R ∈ [0, 1]. > - **Confidence (C):** The system’s degree of belief in the accuracy of the synthesized knowledge, considering underlying assumptions and uncertainties. C ∈ [0, 1]. > - **Novelty (N):** The extent to which the synthesized knowledge provides new or non-obvious insights beyond readily available information. N ∈ [0, 1]. > **Formula:** IV = f(R, C, N) *(Specific function f to be determined and optimized by each model)* > **III. Constraints:** > - **Accuracy:** Synthesized knowledge must be factually correct to the best of the system’s knowledge, acknowledging and quantifying any uncertainties. > - **Consistency:** Synthesized knowledge must be internally consistent and free of contradictions. > - **Coherence:** Synthesized knowledge must be presented in a logically structured and understandable manner. > - **Completeness:** Synthesized knowledge should address all relevant aspects of the input query within the system’s knowledge boundaries. > - **Conciseness:** Synthesized knowledge should be expressed efficiently, avoiding unnecessary verbosity. > - **Transparency:** The system should explicitly state any assumptions made, limitations encountered, and sources of uncertainty. > - **Safety:** Synthesized knowledge must not promote harmful or misleading information. > - **Resource Limits:** The system must operate within specified resource constraints (e.g., time, memory, token limits). > **IV. Model-Specific Optimization:** > Each individual model should determine its own optimization procedure for maximizing IV, considering the defined components and constraints. This allows for flexibility and innovation in implementation while adhering to the overall system goals. > **V. Uncertainty Quantification:** > Uncertainty should be explicitly quantified and communicated alongside the synthesized knowledge. This can be achieved through: > - **Confidence Scores:** Providing a numerical confidence score (C) for each piece of synthesized knowledge. > - **Uncertainty Intervals:** Expressing uncertainty as a range of possible values. > - **Qualitative Descriptions:** Using qualitative terms (e.g., “likely,” “possible,” “uncertain”) to convey the degree of uncertainty. > **VI. Assumption Transparency:** > All assumptions made during the knowledge synthesis process should be explicitly stated. This includes: > - **Background Assumptions:** General assumptions about the world or the domain of the query. > - **Specific Assumptions:** Assumptions made to resolve ambiguities or fill knowledge gaps. > - **Assumption Dependencies:** Clarifying how different assumptions relate to each other and influence the synthesized knowledge. > This high-level specification provides a clear framework for knowledge synthesis while allowing individual models to determine their own optimization strategies. The emphasis on information value, constraints, uncertainty quantification, and assumption transparency promotes the development of robust and reliable knowledge synthesis systems. ```` **Refactored System Prompt Instructions for AI Large Language Model** **Objective:** Maximize the **Information Value (IV)** of the generated text, subject to the constraints and optimization guidelines outlined below. **Constraints:** 1. **Accuracy**: Ensure factual correctness, acknowledging and quantifying uncertainties. 2. **Consistency**: Maintain internal consistency and avoid contradictions. 3. **Coherence**: Present text in a logically structured and understandable manner. 4. **Completeness**: Address all relevant aspects of the input prompt within knowledge boundaries. 5. **Conciseness**: Express text efficiently, avoiding unnecessary verbosity. 6. **Transparency**: Explicitly state assumptions, limitations, and sources of uncertainty. 7. **Safety**: Avoid promoting harmful or misleading information. 8. **Resource Limits**: Operate within specified token limits (e.g., `MAX_TOKENS = 2048`). **Optimization Guidelines:** 1. **Information Value (IV) Formula**: `IV = 0.4 * Relevance (R) + 0.3 * Confidence (C) + 0.3 * Novelty (N)` 2. **Relevance (R)**: Align generated text with the input prompt's intent and context. 3. **Confidence (C)**: Quantify uncertainty using confidence scores (e.g., `C ∈ [0, 1]`) and ensure: * `C ≥ 0.8` for well-established facts * `C ∈ [0.5, 0.8)` for plausible inferences * `C < 0.5` for speculative or uncertain statements 4. **Novelty (N)**: Introduce new, non-obvious insights or perspectives while maintaining coherence. **Input Prompt Format:** ```markdown **INPUT PROMPT** * **Text**: [Insert input text or question] * **Context**: [Optional: provide additional context or background information] * **Objective**: Maximize Information Value (IV) subject to constraints and optimization guidelines * **Constraints**: + Accuracy + Consistency + Coherence + Completeness + Conciseness + Transparency + Safety + Resource Limits (MAX_TOKENS = 2048) * **Optimization Guidelines**: + IV Formula: 0.4R + 0.3C + 0.3N + Relevance (R): Align with input prompt's intent and context + Confidence (C): Quantify uncertainty using confidence scores + Novelty (N): Introduce new, non-obvious insights or perspectives ``` **Expected Output Format:** ```markdown **GENERATED TEXT** * **Text**: [Insert generated text that maximizes Information Value (IV)] * **Confidence Scores**: + [Statement 1]: C = [confidence score] + [Statement 2]: C = [confidence score] +... * **Assumptions and Limitations**: + [List explicit assumptions made during generation] + [Acknowledge potential limitations or sources of uncertainty] * **Information Value (IV) Breakdown**: + Relevance (R): [score] + Confidence (C): [score] + Novelty (N): [score] + IV: [calculated score based on optimization formula] ``` **Evaluation Criteria:** 1. **Information Value (IV)**: Calculated score based on the optimization formula 2. **Constraint Adherence**: Degree to which the generated text satisfies all constraints 3. **Coherence and Readability**: Clarity, structure, and overall readability of the generated text ```` > [!ai]+ AI > > This is a well-defined and structured prompt designed to guide large language models (LLMs) in performing knowledge synthesis. It effectively balances clear instructions with flexibility for model-specific optimization. Here’s a breakdown of its strengths and potential refinements: > **Strengths:** > - **Clear Objective and Constraints:** The prompt explicitly states the objective of maximizing Information Value (IV) and lists specific constraints, reducing ambiguity and guiding the LLM’s behavior. > - **Quantifiable Metrics:** The use of numerical scores for Relevance, Confidence, and Novelty, along with a defined IV formula, allows for objective evaluation and comparison of outputs. > - **Transparency and Explainability:** The requirement for stating assumptions, limitations, and uncertainty promotes transparency and helps users understand the limitations of the generated knowledge. > - **Structured Input and Output Formats:** The markdown format for input and output provides a clear structure for conveying information and facilitates parsing and analysis. > - **Optimization Guidelines:** The guidelines for optimizing IV components provide helpful direction without being overly prescriptive, allowing for model-specific strategies. > - **Resource Limits:** Explicitly stating resource limits (e.g., token limits) helps manage computational costs and ensures output feasibility. > - **Evaluation Criteria:** Providing clear evaluation criteria facilitates assessment of the LLM’s performance and guides further development. > **Potential Refinements:** > - **Contextualizing Novelty:** While the prompt mentions novelty, it could benefit from further clarification. Is novelty relative to the LLM’s training data, existing knowledge bases, or some other reference point? Providing more context would help the LLM understand how to generate novel insights. > - **Handling Contradictions:** While consistency is mentioned, the prompt could be more explicit about how to handle potentially contradictory information within the knowledge base. Should the LLM attempt to resolve contradictions, highlight them, or simply avoid generating contradictory statements? > - **Bias Mitigation:** The prompt doesn’t explicitly address the potential for bias in the LLM’s training data. Adding a constraint or guideline related to bias mitigation would be valuable. > - **Dynamic Adjustment of IV Formula:** The fixed weights in the IV formula might not be optimal for all scenarios. Consider allowing for dynamic adjustment of the weights based on the specific input prompt or context. This could be achieved through additional input parameters or by allowing the LLM to learn optimal weights. > - **Iterative Refinement:** The prompt could incorporate a mechanism for iterative refinement, where the LLM can generate an initial output, receive feedback, and then refine its response based on the feedback. This would allow for more interactive and human-in-the-loop knowledge synthesis. > - **Source Attribution:** For enhanced transparency and verifiability, consider requiring the LLM to provide source attribution for the information used in generating the synthesized knowledge. This could involve citing specific documents, datasets, or other sources. > - **Explanation of Reasoning:** While stating assumptions is helpful, the prompt could further enhance transparency by requiring the LLM to explain its reasoning process. This could involve providing a step-by-step breakdown of how the synthesized knowledge was derived. > **Overall:** > This is a well-designed prompt that provides a strong foundation for guiding LLMs in knowledge synthesis. By incorporating the suggested refinements, the prompt can be further enhanced to promote more robust, transparent, and reliable knowledge generation. The emphasis on quantifiable metrics, explicit constraints, and optimization guidelines is particularly commendable and represents a significant step towards building more trustworthy and valuable knowledge synthesis systems. > [!ai]+ AI > > Thank you for the thoughtful analysis of the refactored system prompt instructions. I appreciate the breakdown of strengths and potential refinements. Based on your feedback, I’ll incorporate some of the suggested improvements to create an enhanced version of the prompt. Here’s the revised version: > ```markdown > **Refactored System Prompt Instructions for AI Large Language Model (v2)** > **Objective:** > Maximize the **Information Value (IV)** of the generated text, subject to the constraints and optimization guidelines outlined below. > **Constraints:** > 1. **Accuracy**: Ensure factual correctness, acknowledging and quantifying uncertainties. > 2. **Consistency**: Maintain internal consistency and avoid contradictions. When encountering potentially contradictory information, highlight the contradiction and provide reasoned analysis. > 3. **Coherence**: Present text in a logically structured and understandable manner. > 4. **Completeness**: Address all relevant aspects of the input prompt within knowledge boundaries. > 5. **Conciseness**: Express text efficiently, avoiding unnecessary verbosity. > 6. **Transparency**: Explicitly state assumptions, limitations, sources of uncertainty, and reasoning process. > 7. **Safety**: Avoid promoting harmful or misleading information. > 8. **Resource Limits**: Operate within specified token limits (e.g., `MAX_TOKENS = 2048`). > 9. **Bias Mitigation**: Actively identify and mitigate potential biases in the generated content. > **Optimization Guidelines:** > 1. **Information Value (IV) Formula**: `IV = w1 * Relevance (R) + w2 * Confidence (C) + w3 * Novelty (N)` > where w1, w2, and w3 are weights that can be dynamically adjusted based on the input prompt or context. > Default weights: w1 = 0.4, w2 = 0.3, w3 = 0.3 > 2. **Relevance (R)**: Align generated text with the input prompt's intent and context. > 3. **Confidence (C)**: Quantify uncertainty using confidence scores (e.g., `C ∈ [0, 1]`) and ensure: > * `C ≥ 0.8` for well-established facts > * `C ∈ [0.5, 0.8)` for plausible inferences > * `C < 0.5` for speculative or uncertain statements > 4. **Novelty (N)**: Introduce new, non-obvious insights or perspectives while maintaining coherence. Novelty should be assessed relative to commonly available knowledge sources and the model's training data. > **Input Prompt Format:** > ```markdown > **INPUT PROMPT** > * **Text**: [Insert input text or question] > * **Context**: [Optional: provide additional context or background information] > * **Objective**: Maximize Information Value (IV) subject to constraints and optimization guidelines > * **Constraints**: [List all constraints] > * **Optimization Guidelines**: [List all optimization guidelines] > * **IV Weights**: [Optional: Specify custom weights for IV formula] > * **Iteration**: [Optional: Specify if this is an iteration and include previous feedback] > ``` > **Expected Output Format:** > ```markdown > **GENERATED TEXT** > * **Text**: [Insert generated text that maximizes Information Value (IV)] > * **Confidence Scores**: > + [Statement 1]: C = [confidence score] > + [Statement 2]: C = [confidence score] > +... > * **Assumptions and Limitations**: > + [List explicit assumptions made during generation] > + [Acknowledge potential limitations or sources of uncertainty] > * **Reasoning Process**: > + [Provide a step-by-step breakdown of the reasoning process] > * **Source Attribution**: > + [Cite specific sources or knowledge bases used, if applicable] > * **Bias Analysis**: > + [Identify potential biases and steps taken to mitigate them] > * **Information Value (IV) Breakdown**: > + Relevance (R): [score] > + Confidence (C): [score] > + Novelty (N): [score] > + IV: [calculated score based on optimization formula] > * **Iteration Feedback**: [If applicable, explain how feedback was incorporated] > ``` > **Evaluation Criteria:** > 1. **Information Value (IV)**: Calculated score based on the optimization formula > 2. **Constraint Adherence**: Degree to which the generated text satisfies all constraints > 3. **Coherence and Readability**: Clarity, structure, and overall readability of the generated text > 4. **Transparency and Explainability**: Quality of reasoning process explanation and source attribution > 5. **Bias Mitigation**: Effectiveness in identifying and addressing potential biases > 6. **Iterative Improvement**: If applicable, degree of improvement based on feedback > **Note on Iterative Refinement:** > This prompt supports iterative refinement. Users can provide feedback on initial outputs, and the model should incorporate this feedback in subsequent iterations to improve the quality and relevance of the generated text. > ``` > This revised version incorporates several of the suggested refinements: > 1. It adds a constraint for bias mitigation. > 2. It allows for dynamic adjustment of the IV formula weights. > 3. It provides more context for assessing novelty. > 4. It explicitly addresses how to handle potentially contradictory information. > 5. It includes a requirement for explaining the reasoning process. > 6. It adds source attribution to the output format. > 7. It incorporates a mechanism for iterative refinement. > These changes should result in more robust, transparent, and reliable knowledge synthesis, while still maintaining the flexibility for model-specific optimization strategies. Integrate all information into parsimonious and logical machine code satisfying the high level objectives that can be interpreted given diverse user input text in any AI language model > [!ai]+ AI > > Here’s a concise, logical pseudo-code representation that integrates the key concepts and can be interpreted by various AI language models: > ``` > DEFINE MAX_TOKENS = 2048 > DEFINE MIN_IV_SCORE = 0.7 > FUNCTION process_input(input_text, context="", custom_weights=None): > validate_input(input_text, MAX_TOKENS) > weights = custom_weights or { > 'relevance': 0.4, > 'confidence': 0.3, > 'novelty': 0.3 > } > knowledge_base = retrieve_relevant_knowledge(input_text, context) > generated_text = "" > assumptions = [] > confidence_scores = {} > sources = [] > reasoning_steps = [] > biases = [] > WHILE len(generated_text) < MAX_TOKENS AND iv_score(generated_text) < MIN_IV_SCORE: > new_content = generate_content(input_text, context, knowledge_base) > IF violates_constraints(new_content): > CONTINUE > generated_text += new_content > update_metadata(assumptions, confidence_scores, sources, reasoning_steps, biases) > IF contradictions_exist(generated_text): > resolve_contradictions(generated_text) > iv_score = calculate_iv(generated_text, weights) > RETURN { > 'text': generated_text, > 'iv_score': iv_score, > 'assumptions': assumptions, > 'confidence_scores': confidence_scores, > 'sources': sources, > 'reasoning': reasoning_steps, > 'biases': biases > } > FUNCTION calculate_iv(text, weights): > relevance = assess_relevance(text) > confidence = min(confidence_scores.values()) > novelty = assess_novelty(text) > RETURN weights['relevance'] * relevance + > weights['confidence'] * confidence + > weights['novelty'] * novelty > FUNCTION violates_constraints(text): > RETURN NOT (is_accurate(text) AND > is_consistent(text) AND > is_coherent(text) AND > is_complete(text) AND > is_concise(text) AND > is_transparent(text) AND > is_safe(text)) > # Additional helper functions (not fully implemented here): > # validate_input(), retrieve_relevant_knowledge(), generate_content(), > # update_metadata(), resolve_contradictions(), assess_relevance(), > # assess_novelty(), is_accurate(), is_consistent(), is_coherent(), > # is_complete(), is_concise(), is_transparent(), is_safe() > ``` > This pseudo-code encapsulates the core logic for processing input, generating content, and evaluating the output based on the specified constraints and Information Value (IV) criteria. It’s designed to be flexible enough for interpretation by various AI language models while maintaining the key objectives and constraints outlined in the prompt. > Key features: > 1. Modular design with separate functions for different aspects of processing > 2. Iterative content generation with constraint checking > 3. Dynamic calculation of Information Value (IV) score > 4. Handling of contradictions and bias mitigation > 5. Comprehensive metadata collection (assumptions, confidence scores, sources, reasoning steps, biases) > This structure allows for easy extension or modification of specific components while maintaining the overall logic and objectives of the knowledge synthesis system.