Retrieval-Augmented Generation - Machine State | ARSA Technology

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Retrieval-Augmented Generation

A collection of 6 posts

Revolutionizing Healthcare: How RAG-Enhanced LLMs are Building Intelligent Assistants

Revolutionizing Healthcare: How RAG-Enhanced LLMs are Building Intelligent Assistants

Explore PriHA, a RAG-enhanced LLM framework designed for primary healthcare assistance. Learn how it overcomes AI limitations to deliver accurate, localized medical information, improving patient self-management and reducing healthcare costs for an aging population.

Building Trust in the Skies: How AI and Knowledge Graphs Revolutionize Aviation Safety

Aviation Safety

Building Trust in the Skies: How AI and Knowledge Graphs Revolutionize Aviation Safety

Explore a novel framework combining Large Language Models with Knowledge Graphs to create verifiable, trustworthy AI for aviation safety, mitigating hallucination and ensuring regulatory compliance.

Beyond the Hype: Why PDF Preprocessing is Critical for High-Accuracy RAG Systems

Retrieval-Augmented Generation

Beyond the Hype: Why PDF Preprocessing is Critical for High-Accuracy RAG Systems

Discover how robust PDF conversion and smart chunking strategies are paramount for Retrieval-Augmented Generation (RAG) system accuracy, especially for sensitive and non-English documents.

AI That Explains Itself: The Rise of Interpretable, Training-Free Systems for Dynamic Insights

Interpretable AI

AI That Explains Itself: The Rise of Interpretable, Training-Free Systems for Dynamic Insights

Explore MERIT, a framework enabling AI systems to provide transparent, reasoned insights without costly retraining. Discover how memory-enhanced retrieval transforms AI for dynamic, interpretable decision-making in enterprises.

Enhancing LLM Reasoning: A New Benchmark for Hybrid Knowledge Integration

Enhancing LLM Reasoning: A New Benchmark for Hybrid Knowledge Integration

Explore HybridRAG-Bench, a new framework evaluating how retrieval-augmented models truly reason over hybrid knowledge, overcoming pretraining contamination for reliable AI deployment.