TorchOpera: A Compound AI System for LLM Safety
Shanshan Han, Zijian Hu, Alay Dilipbhai Shah, Han Jin, Yuhang Yao,, Dimitris Stripelis, Zhaozhuo Xu, Chaoyang He

TL;DR
TorchOpera is a comprehensive AI system designed to improve the safety, relevance, and quality of Large Language Model outputs through grounding, rule-based modifications, and safety mechanisms, suitable for real-world applications.
Contribution
It introduces a novel compound AI system integrating grounding, safety, and quality control to enhance LLM performance and safety efficiently.
Findings
Ensures prompt safety and contextual grounding.
Improves response relevance and quality.
Maintains efficiency in real-world settings.
Abstract
We introduce TorchOpera, a compound AI system for enhancing the safety and quality of prompts and responses for Large Language Models. TorchOpera ensures that all user prompts are safe, contextually grounded, and effectively processed, while enhancing LLM responses to be relevant and high quality. TorchOpera utilizes the vector database for contextual grounding, rule-based wrappers for flexible modifications, and specialized mechanisms for detecting and adjusting unsafe or incorrect content. We also provide a view of the compound AI system to reduce the computational cost. Extensive experiments show that TorchOpera ensures the safety, reliability, and applicability of LLMs in real-world settings while maintaining the efficiency of LLM responses.
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Taxonomy
TopicsNuclear Engineering Thermal-Hydraulics · Fault Detection and Control Systems · Adversarial Robustness in Machine Learning
