Beyond Retrieval: Modeling Confidence Decay and Deterministic Agentic Platforms in Generative Engine Optimization

1. 1Identify the limitations of Retrieval-Augmented Generation (RAG) in Generative Engine Optimization
2. 2Analyze the probabilistic flaws of RAG, including hallucinations and the 'zero-click' paradox
3. 3Develop a new approach to GEO that incorporates deterministic agentic platforms and models confidence decay
4. 4Evaluate the effectiveness of the proposed approach in establishing sustainable commercial trust

AI engineers and researchers working on Large Language Models (LLMs) and digital marketing strategies can benefit from this research, as it provides a new perspective on Generative Engine Optimization

💡 Current RAG-based strategies for Generative Engine Optimization have inherent probabilistic flaws that can be addressed with a new approach incorporating deterministic agentic platforms and confidence decay modeling

🚀 Beyond RAG: New approach to Generative Engine Optimization addresses probabilistic flaws #LLMs #GEO