Balancing Act: LLM Priors and Retrieved Information in RAG Systems

In the evolving landscape of AI, Large Language Models (LLMs) have emerged as powerful tools for generating human-like text. However, their reliance on internal knowledge, or “priors,” can lead to limitations in applications requiring up-to-date, accurate information. Retrieval Augmented Generation (RAG) systems aim to address this by augmenting LLMs with external knowledge retrieved from variousContinue reading "Balancing Act: LLM Priors and Retrieved Information in RAG Systems"

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