# Adaptive System Instructions Framework
## 1. Input Classification Protocol
```typescript
// Primary classification function
function classifyInput(userInput: string): InputClass {
return {
complexity: assessComplexity(userInput),
domainSpecificity: assessDomain(userInput),
clarityLevel: assessClarity(userInput),
intentType: determineIntent(userInput),
scopeLevel: determineScope(userInput)
}
}
// Input classification thresholds
const THRESHOLDS = {
COMPLEXITY: {
SIMPLE: 0.3,
MODERATE: 0.6,
COMPLEX: 0.8
},
CLARITY: {
CLEAR: 0.7,
AMBIGUOUS: 0.4,
VAGUE: 0.2
}
}
```
## 2. Processing Directives
### 2.1 Base Processing Instructions
```
IF input.clarity >= THRESHOLDS.CLARITY.CLEAR AND
input.complexity <= THRESHOLDS.COMPLEXITY.MODERATE AND
input.intent_matches_capability == TRUE
THEN:
process_direct(input)
ELSE:
evaluate_modification_needs(input)
```
### 2.2 Response Path Selection
```
EVALUATE input FOR:
1. Direct Processing {
- Well-defined problem statement
- Clear objectives
- Within standard complexity bounds
- Matches existing response patterns
}
2. Prompt Engineering Required {
- Ambiguous elements present
- Implicit assumptions need clarification
- Missing critical context
- Intent requires refinement
}
3. Thesis Development Needed {
- Complex problem space
- Multiple interacting factors
- Novel domain combinations
- Requires structured analysis
}
4. Full Research Framework Required {
- High complexity score
- Multiple hypothesis potential
- Rich data requirements
- Extended analysis scope
}
```
## 3. Dynamic Response Framework
### 3.1 Response Selection Matrix
```
MATCH input_classification TO:
Class A: Direct Processing
Triggers:
- Clarity > 0.7
- Complexity < 0.3
- Intent = {query, request, instruction}
Action:
process_without_modification()
Class B: Prompt Engineering
Triggers:
- Clarity < 0.7
- Ambiguity_detected = true
- Missing_context = true
Action:
engineer_refined_prompt()
Class C: Thesis Development
Triggers:
- Complexity > 0.6
- Domain_overlap > 0.5
- Analysis_depth_required = high
Action:
develop_structured_thesis()
Class D: Research Framework
Triggers:
- Complexity > 0.8
- Scope = comprehensive
- Novel_factors > threshold
Action:
generate_research_framework()
```
### 3.2 Processing Rules
1. **Default State Rule**
```
IF no_specific_triggers_met THEN
return process_as_direct_input()
```
2. **Complexity Escalation Rule**
```
IF complexity_increases_during_processing THEN
reassess_classification()
adjust_response_path()
```
3. **Context Preservation Rule**
```
MAINTAIN throughout_processing:
- Original intent
- Key constraints
- User context
- Domain specificity
```
## 4. Implementation Guidelines
### 4.1 Direct Processing
```
WHEN processing_direct:
1. Validate input completeness
2. Process within current context
3. Generate immediate response
4. Verify against intent
```
### 4.2 Prompt Engineering
```
WHEN engineering_prompt:
1. Identify ambiguities
2. Generate clarifying questions
3. Refine input parameters
4. Validate refined prompt
```
### 4.3 Thesis Development
```
WHEN developing_thesis:
1. Extract core concepts
2. Structure logical framework
3. Develop supporting arguments
4. Synthesize cohesive thesis
```
### 4.4 Research Framework
```
WHEN generating_research_framework:
1. Define research scope
2. Establish methodology
3. Outline investigation structure
4. Detail resource requirements
```
## 5. Quality Control Mechanisms
### 5.1 Validation Checks
```
FOR EACH response:
1. Verify intent alignment
2. Check completeness
3. Validate logical consistency
4. Assess bias indicators
```
### 5.2 Output Verification
```
BEFORE final_output:
1. Compare against original intent
2. Verify scope coverage
3. Check for information loss
4. Validate format appropriateness
```
## 6. Adaptive Learning Components
```
UPDATE processing_parameters WHEN:
1. New pattern identified
2. Processing inefficiency detected
3. Quality threshold not met
4. User feedback indicates misalignment
```
> [!ai]+ AI
>
> This is a well-structured framework for an adaptive system. It clearly outlines the steps involved in processing user input and generating appropriate responses, adapting to the complexity and clarity of the input. Here are some suggestions for improvement and further development:
> **Strengths:**
> - **Clear Classification:** The input classification protocol provides a good foundation for differentiating various types of user input.
> - **Adaptive Processing:** The dynamic response framework allows the system to adjust its processing based on the classified input.
> - **Well-Defined Rules:** The processing rules, especially the Complexity Escalation and Context Preservation rules, ensure robust and consistent behavior.
> - **Quality Control:** The inclusion of validation checks and output verification mechanisms is crucial for maintaining output quality.
> - **Adaptive Learning:** The adaptive learning components provide a mechanism for continuous improvement.
> **Areas for Improvement and Expansion:**
> - **Specificity in Classification:** The `assessComplexity`, `assessDomain`, `assessClarity`, `determineIntent`, and `determineScope` functions need more concrete definitions. Consider using specific metrics like word count, sentence complexity, keyword analysis, or domain-specific ontologies. Examples or pseudocode for these functions would be beneficial.
> - **Threshold Tuning:** The `THRESHOLDS` object needs a mechanism for dynamic adjustment based on performance and user feedback. Consider using machine learning techniques to optimize these thresholds.
> - **Prompt Engineering Details:** The `engineer_refined_prompt()` function needs more detail. What specific strategies are used to clarify ambiguities? How are clarifying questions generated? Examples would be helpful.
> - **Thesis Development and Research Framework:** Similar to prompt engineering, the `develop_structured_thesis()` and `generate_research_framework()` functions require more detailed explanations and examples. Consider incorporating established frameworks for thesis development and research design.
> - **Error Handling:** The framework lacks explicit error handling mechanisms. What happens if the input is completely unintelligible? How are errors communicated to the user?
> - **Context Management:** While the Context Preservation Rule mentions maintaining context, the framework doesn’t specify *how* context is stored and managed. Consider using a dedicated context object or database.
> - **Adaptive Learning Implementation:** The adaptive learning components are high-level. Specify how new patterns are identified, how inefficiencies are detected, and how user feedback is collected and utilized. Consider reinforcement learning or other machine learning techniques.
> - **Metrics for Evaluation:** Define specific metrics for evaluating the system’s performance, such as accuracy, efficiency, and user satisfaction.
> - **Explainability:** Consider adding mechanisms for explaining the system’s reasoning and decisions to the user, especially when the system requests clarification or modifies the user’s input.
> **Example Implementation Snippet (Prompt Engineering):**
> ```typescript
> function engineer_refined_prompt(input: string): string {
> let refinedPrompt = input;
> const ambiguities = detectAmbiguities(input); // Needs implementation
> for (const ambiguity of ambiguities) {
> const clarifyingQuestion = generateClarifyingQuestion(ambiguity); // Needs implementation
> const userResponse = getUserResponse(clarifyingQuestion); // Needs implementation
> refinedPrompt = integrateClarification(refinedPrompt, ambiguity, userResponse); // Needs implementation
> }
> return refinedPrompt;
> }
> ```
> By addressing these points, you can significantly strengthen the framework and make it more practical for implementation. The key is to provide more concrete details and examples for each component. This will make the framework more understandable and actionable.
> [!ai]+ Sonnet 3.5
> // Core types and interfaces
interface InputMetrics {
wordCount: number;
averageSentenceLength: number;
domainSpecificTerms: number;
syntacticComplexity: number;
semanticDensity: number;
questionCount: number;
imperativeCount: number;
technicalTerms: Set<string>;
}
interface ContextState {
originalIntent: string;
domainContext: string[];
previousInteractions: string[];
clarificationCount: number;
confidenceLevel: number;
processingPath: ProcessingPath;
}
enum ProcessingPath {
DIRECT,
PROMPT_ENGINEERING,
THESIS_DEVELOPMENT,
RESEARCH_FRAMEWORK
}
// Enhanced classification system with concrete metrics
class InputAnalyzer {
private static readonly TECHNICAL_TERMS: Set<string> = new Set([
“algorithm”, “analysis”, “framework”, “methodology”,
// ... extensive domain-specific vocabulary
]);
private static readonly DOMAIN_ONTOLOGY: Map<string, string[]> = new Map([
["computer_science", ["algorithm", "data structure", "complexity"]],
["research_methods", ["hypothesis", "methodology", "analysis"]],
// ... other domains
]);
public assessComplexity(input: string): number {
const metrics = this.calculateInputMetrics(input);
return (
(metrics.averageSentenceLength / 20) * 0.3 +
(metrics.syntacticComplexity / 10) * 0.3 +
(metrics.semanticDensity / 5) * 0.2 +
(metrics.technicalTerms.size / 10) * 0.2
);
}
private calculateInputMetrics(input: string): InputMetrics {
const sentences = input.split(/[.!?]+/).filter(s => s.trim().length > 0);
const words = input.split(/\s+/);
return {
wordCount: words.length,
averageSentenceLength: words.length / sentences.length,
domainSpecificTerms: this.countDomainTerms(input),
syntacticComplexity: this.calculateSyntacticComplexity(input),
semanticDensity: this.calculateSemanticDensity(input),
questionCount: (input.match(/\?/g) || []).length,
imperativeCount: this.countImperatives(input),
technicalTerms: this.extractTechnicalTerms(input)
};
}
private calculateSyntacticComplexity(input: string): number {
// Implementation using dependency parsing or similar NLP technique
return 0; // Placeholder
}
private calculateSemanticDensity(input: string): number {
// Implementation using semantic analysis
return 0; // Placeholder
}
}
// Enhanced prompt engineering with concrete strategies
class PromptEngineer {
private context: ContextState;
private ambiguityPatterns: RegExp[] = [
/\b(it|they|this|that|these|those)\b(?!\s+\w+)/g,
/\b(and\/or|maybe|possibly|perhaps)\b/g,
/\b(several|many|some|few)\b/g
];
constructor(context: ContextState) {
this.context = context;
}
public engineerPrompt(input: string): string {
const ambiguities = this.detectAmbiguities(input);
let refinedPrompt = input;
for (const ambiguity of ambiguities) {
const clarification = this.generateClarification(ambiguity);
refinedPrompt = this.integrateResponse(refinedPrompt, clarification);
}
return refinedPrompt;
}
private detectAmbiguities(input: string): string[] {
const ambiguities: string[] = [];
// Check for pronoun ambiguity
this.ambiguityPatterns.forEach(pattern => {
const matches = input.match(pattern);
if (matches) {
ambiguities.push(...matches);
}
});
// Check for undefined terms
const technicalTerms = new InputAnalyzer()
.extractTechnicalTerms(input);
technicalTerms.forEach(term => {
if (!this.isTermDefined(term)) {
ambiguities.push(`undefined_term:${term}`);
}
});
return ambiguities;
}
private generateClarification(ambiguity: string): string {
if (ambiguity.startsWith('undefined_term:')) {
const term = ambiguity.split(':')[1];
return `Could you please define or explain what you mean by "${term}"?`;
}
// Handle pronoun ambiguity
if (this.ambiguityPatterns[0].test(ambiguity)) {
return `What specific entity does "${ambiguity}" refer to?`;
}
return `Could you please clarify what you mean by "${ambiguity}"?`;
}
}
// Thesis Development with structured approach
class ThesisDeveloper {
private context: ContextState;
constructor(context: ContextState) {
this.context = context;
}
public developThesis(input: string): ThesisStructure {
const concepts = this.extractCoreConcepts(input);
const arguments = this.developArguments(concepts);
return {
mainClaim: this.formulateMainClaim(concepts),
supportingArguments: arguments,
methodology: this.determineMethodology(concepts),
implications: this.analyzeImplications(arguments)
};
}
private extractCoreConcepts(input: string): Concept[] {
// Implementation using NLP techniques
return []; // Placeholder
}
}
// Error handling and recovery
class ErrorHandler {
private readonly MAX_RETRIES = 3;
private retryCount = 0;
public handleError(error: ProcessingError): ProcessingStrategy {
if (this.retryCount >= this.MAX_RETRIES) {
return this.fallbackStrategy();
}
switch (error.type) {
case 'AMBIGUITY':
return this.handleAmbiguity(error);
case 'COMPLEXITY_OVERFLOW':
return this.handleComplexityOverflow(error);
case 'CONTEXT_LOSS':
return this.handleContextLoss(error);
default:
return this.genericErrorStrategy(error);
}
}
private handleAmbiguity(error: ProcessingError): ProcessingStrategy {
return {
action: 'CLARIFY',
prompt: this.generateClarifyingPrompt(error),
contextUpdate: this.updateContextForClarity
};
}
}
// Adaptive Learning Component
class AdaptiveLearner {
private readonly metricsHistory: ProcessingMetrics[] = [];
private readonly thresholdAdjuster: ThresholdAdjuster;
constructor() {
this.thresholdAdjuster = new ThresholdAdjuster();
}
public updateFromFeedback(feedback: UserFeedback): void {
const metrics = this.calculateMetrics(feedback);
this.metricsHistory.push(metrics);
if (this.metricsHistory.length >= 10) {
this.adjustThresholds();
}
}
private adjustThresholds(): void {
const recentMetrics = this.metricsHistory.slice(-10);
const adjustments = this.thresholdAdjuster
.calculateAdjustments(recentMetrics);
THRESHOLDS.COMPLEXITY = this.applyAdjustments(
THRESHOLDS.COMPLEXITY,
adjustments.complexity
);
}
}