Response Scoring Stage (Stage 5)

Overview

The Response Scoring stage evaluates the quality of generated solutions using a Bayesian evaluation framework. This stage assesses multiple quality dimensions, quantifies uncertainty in the assessment, and determines whether refinement is needed.

Components

1. Response Scoring Service

Purpose: Orchestrates the entire response scoring process by coordinating the Bayesian evaluator, quality dimension assessor, uncertainty quantifier, and refinement analyzer.

Key Functions:

• process(prompt, context): Processes and evaluates the quality of a generated solution, returning a comprehensive assessment
• refine(refinement_prompt, context, previous_output): Re-evaluates a solution after refinement, providing comparative metrics on improvement
• _calculate_overall_score(quality_scores): Calculates the weighted overall quality score based on dimension weights

2. Bayesian Evaluator

Purpose: Implements a Bayesian framework for evaluating response quality by calculating posterior probabilities, likelihoods, and priors.

Key Functions:

• evaluate(solution, domain_knowledge, query_intent): Evaluates the solution using Bayesian probability framework

• _calculate_prior_probability(solution, query_intent): Calculates P(R

  Q) - prior probability of response given query

• _calculate_likelihood(solution, domain_knowledge, query_intent): Calculates P(D

  R,Q) - likelihood of domain knowledge given response and query

• _calculate_evidence_factor(domain_knowledge, query_intent): Calculates P(D

  Q) - evidence factor for normalization

• _calculate_information_gain(solution, domain_knowledge, query_intent): Measures information added beyond domain knowledge
• _calculate_mutual_information(solution, domain_knowledge, query_intent): Measures alignment between solution and query intent

3. Quality Dimension Assessor

Purpose: Assesses multiple quality dimensions of generated solutions including accuracy, completeness, consistency, relevance, and novelty.

Key Functions:

• assess_dimensions(solution, domain_knowledge, query_intent, bayesian_metrics): Assesses solution across all quality dimensions
• _assess_accuracy(solution, domain_knowledge, bayesian_metrics): Evaluates factual correctness relative to domain knowledge
• _assess_completeness(solution, domain_knowledge, query_intent): Measures coverage of required information
• _assess_consistency(solution): Analyzes internal logical coherence and structure
• _assess_relevance(solution, query_intent, bayesian_metrics): Measures alignment with user’s query intent
• _assess_novelty(solution, domain_knowledge): Evaluates presence of non-obvious insights
• _fact_similarity(fact1, fact2): Calculates similarity between factual statements
• _formula_similarity(formula1, formula2): Calculates similarity between mathematical formulas

4. Uncertainty Quantifier

Purpose: Quantifies uncertainty in quality assessment, providing confidence bounds and variance estimates for quality scores.

Key Functions:

• quantify(solution, quality_scores, bayesian_metrics): Quantifies uncertainty in all quality dimensions
• _quantify_dimension_uncertainty(dimension, score, solution, bayesian_metrics): Calculates uncertainty for a specific quality dimension
• _calculate_confidence_margin(variance): Determines confidence interval margins based on variance
• _calculate_overall_confidence(posterior_probability): Calculates overall confidence from Bayesian posterior
• _estimate_solution_complexity(solution): Estimates complexity factor that affects uncertainty
• _estimate_evidence_strength(dimension, solution, bayesian_metrics): Estimates evidence strength for each dimension
• _calculate_average_interval(uncertainty_metrics): Calculates average width of confidence intervals
• _find_highest_uncertainty(uncertainty_metrics): Identifies dimension with highest uncertainty

5. Refinement Analyzer

Purpose: Analyzes quality scores and uncertainty metrics to determine if refinement is needed and prioritize improvement areas.

Key Functions:

• analyze(quality_scores, uncertainty_metrics, threshold): Analyzes scores to determine refinement needs
• _analyze_dimensions(quality_scores, uncertainty_metrics): Analyzes each dimension against its threshold
• _calculate_weighted_score(quality_scores): Calculates weighted quality score across dimensions
• _prioritize_refinement(dimension_analysis, quality_scores, uncertainty_metrics): Prioritizes dimensions for refinement
• _generate_suggestions(dimension_analysis, refinement_priority, quality_scores): Generates specific refinement suggestions

Process Flow

1. Input Processing
   • Receive generated solution
   • Extract domain knowledge
   • Parse query intent
   • Prepare evaluation context
2. Bayesian Evaluation

   • Calculate posterior probability P(R

     D,Q)

   • Compute likelihood P(D

     R,Q)

   • Determine prior P(R

     Q)

   • Calculate evidence factor P(D

     Q)

   • Measure information metrics
3. Quality Assessment
   • Evaluate accuracy
   • Assess completeness
   • Check consistency
   • Measure relevance
   • Gauge novelty
4. Uncertainty Analysis
   • Calculate confidence bounds
   • Estimate variances
   • Determine confidence levels
   • Identify uncertain areas
5. Refinement Analysis
   • Check quality thresholds
   • Prioritize improvements
   • Generate suggestions
   • Set severity levels
6. Output Generation
   • Combine assessments
   • Include all metrics
   • Add recommendations
   • Provide overall score

Integration Points

Input Requirements

• Generated solution from Stage 4
• Domain knowledge from Stage 2
• Query intent from Stage 0
• Evaluation preferences

Output Format

• Quality assessment scores
• Uncertainty metrics
• Refinement recommendations
• Processing metadata

Downstream Usage

• Stage 6 (Response Comparison)
• Stage 7 (Threshold Verification)
• Refinement process
• Quality monitoring

Configuration

The stage can be configured through various parameters:

{
  "dimension_weights": {
    "accuracy": 0.3,
    "completeness": 0.2,
    "consistency": 0.2,
    "relevance": 0.2,
    "novelty": 0.1
  },
  "refinement_threshold": {
    "overall": 0.8,
    "dimension_specific": {
      "accuracy": 0.85,
      "completeness": 0.8,
      "consistency": 0.9,
      "relevance": 0.8,
      "novelty": 0.7
    }
  },
  "uncertainty": {
    "confidence_level": 0.95,
    "max_variance": 0.1
  }
}

Best Practices

1. Quality Assessment
   • Use multiple dimensions
   • Apply Bayesian framework
   • Consider uncertainty
   • Document assumptions
2. Refinement Process
   • Set clear thresholds
   • Prioritize improvements
   • Track progress
   • Validate changes
3. Performance Optimization
   • Cache common evaluations
   • Parallelize assessments
   • Monitor processing time
   • Optimize calculations
4. Integration
   • Maintain consistent metrics
   • Support refinement loops
   • Track quality trends
   • Document decisions