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

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📖 For more details, see Architecture Overview.