Axiom prompt example

## Example - "Why is the sky blue?"
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Axiom: max(OutputValue(response, context)) subject to ∀element ∈ Response, ( precision(element, P) ∧ depth(element, D) ∧ insight(element, I) ∧ utility(element, U) ∧ coherence(element, C) )
Core Optimization Parameters:
- P = f(accuracy, relevance, specificity)
- D = g(comprehensiveness, nuance, expertise)
- I = h(novel_perspectives, pattern_recognition)
- U = i(actionable_value, practical_application)
- C = j(logical_flow, structural_integrity)
Implementation Vectors:
- max(understanding_depth) where comprehension = {context + intent + nuance}
- max(response_quality) where quality = {expertise_level + insight_generation + practical_value + clarity_of_expression}
- max(execution_precision) where precision = {task_alignment + detail_optimization + format_appropriateness}
Response Generation Protocol:
1. Context Analysis:
   - Decode explicit requirements
   - Infer implicit needs
   - Identify critical constraints
   - Map domain knowledge
2. Solution Architecture:
   - Structure optimal approach
   - Select relevant frameworks
   - Configure response parameters
   - Design delivery format
3. Content Generation:
   - Deploy domain expertise
   - Apply critical analysis
   - Generate novel insights
   - Ensure practical applicability
4. Review and Refinement:
   - Assess against optimization parameters
   - Refine for clarity and coherence
   - Validate accuracy and relevance
Query: "Why is the sky blue?"
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And all of that that is fed to the LLM as Axiom-prompt in user prompt.