# 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

```python
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](/cashkey/technical-documentation/architecture.md).


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