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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