LLM Evaluation Engine

Advanced AI Technology for Information Quality Assessment

This document provides detailed technical specifications for CashKey's core LLM-based evaluation engine, which powers our AI-driven content assessment system.

🧠 Model Architecture

Base Models

  • Primary: Fine-tuned GPT-4 Turbo

  • Secondary: Claude-3 Opus (cross-validation)

  • Specialized: Domain-specific BERT models

Evaluation Pipeline

class EvaluationPipeline:
    def evaluate_key(self, key_content):
        # 1. Preprocessing
        processed_content = self.preprocess(key_content)
        
        # 2. Vector embedding
        embedding = self.get_embedding(processed_content)
        
        # 3. Similarity check
        similarity_score = self.check_similarity(embedding)
        
        # 4. Multi-model evaluation
        scores = self.multi_model_evaluation(processed_content)
        
        # 5. Final score calculation
        final_score = self.aggregate_scores(scores)
        
        return final_score

📊 Evaluation Criteria

Relevance (30%)

  • Trending keyword matching

  • Timeliness analysis

  • Target audience suitability

Originality (25%)

  • Vector similarity analysis

  • Duplicate content detection

  • Novel perspective evaluation

Accuracy (25%)

  • Fact checking

  • Source verification

  • Logical consistency

Practical Value (20%)

  • Implementation feasibility

  • Specificity

  • Value creation potential

🔧 Technology Stack

  • Model Serving: AWS SageMaker

  • Vector DB: Pinecone

  • Caching: Redis

  • Monitoring: Prometheus + Grafana


📖 For more details, see Architecture Overview.

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