A comprehensive system for designing, evaluating, and optimizing prompts with advanced cognitive architecture principles, epistemic integrity mechanisms, and context engineering approaches.
Explore FrameworkMental model preservation and cognitive archaeology
Advanced principles for preserving mental models, enabling cognitive archaeology, and maintaining cognitive transparency in prompt engineering.
Semantic integrity and execution verification
Formal approaches for defining context bundles, maintaining semantic integrity, and creating verifiable execution pathways with comprehensive audit trails.
Cognitive and epistemic assessment
Advanced evaluation framework incorporating cognitive dimensions, epistemic integrity metrics, and context engineering principles.
Cognitive integrity through minimization
Structured methodology for creating minimal prompts that maintain cognitive integrity, context anchoring, and semantic stability.
Topological grammar and drift resistance
Advanced assistant with topological grammar analysis, cognitive archaeology support, and drift-resistant grammatical constructions.
Cross-component communication and validation
Unified system with cross-component communication, shared services, and recursive self-improvement mechanisms.
Advanced techniques to preserve the underlying mental models that inform prompt creation and interpretation, preventing decay over time and through iterations.
Formal framework for defining context bundles and ensuring verifiable execution from context definition to output, with comprehensive audit trails.
Methods to define and enforce semantic boundaries, preventing drift and maintaining knowledge integrity across iterations and contexts.
Systematic approach to recovering the mental models and reasoning that informed prompt creation, enabling understanding of original intent.
Advanced grammatical analysis that detects semantic phase transitions and maintains structural integrity across transformations.
Deliberate markers of past failures and their resolutions, preventing repeating mistakes by preserving institutional memory.
// Context Bundle Definition
{
"context_bundle_id": "financial_report_analysis_v1.3",
"goal": {
"primary": "Extract key financial metrics from quarterly report",
"secondary": ["Identify growth trends", "Flag regulatory concerns"]
},
"context_sources": {
"primary_documents": ["Q2_2025_Financial_Report.pdf"],
"reference_documents": ["Financial_Metrics_Definitions.md"],
"knowledge_boundaries": ["Limited to explicit content in documents"]
},
"constraints": [
{"type": "FactualAccuracy", "threshold": "High", "verification": "Source_Citation"},
{"type": "SemanticDrift", "max_coefficient": 0.05, "verification": "Embedding_Comparison"}
],
"success_criteria": [
{"metric": "Metric_Coverage", "threshold": "95%"},
{"metric": "Citation_Accuracy", "threshold": "100%"}
]
}
DocumentAnalysis[ Context: 'Q2_2025_Financial_Report', Goal: 'ExtractKeyMetrics_Comprehensive', Constraint: 'FactualAccuracy_SDCLessThan0.05' ]
Semantic Drift Coefficient: 0.04 (Within acceptable range)
Establish core components and basic integration
Connect components through standardized interfaces
Optimize metrics and enhance service orchestration
Apply system to improve itself