Unveiling Rag Modern Rag: Enhancing Data Processing with Language Models
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In a riveting tale of modern innovation, Rag Modern Rag burst onto the scene in 2022, following the groundbreaking Retrieval Augmented Generation paper from 2021 or 2020. This ingenious concept proposed embedding documents for efficient retrieval, setting the stage for a new era in data processing. As more enthusiasts delved into the world of LFS, the true potential of this idea began to shine through, sparking a wave of excitement and creativity.
The initial version of Rag did not utilize embeddings, instead opting to let the language model take the reins in independent reasoning. This bold approach aimed to push the boundaries of what AI systems could achieve, challenging the status quo in data processing. The current state of L Index reflects this philosophy, integrating language models into both data ingestion and generation processes for a comprehensive and seamless workflow.
While traditional Rag pipelines rely on language models for answer synthesis at the end of the process, there is untapped potential in leveraging LMs at the beginning stages. By incorporating language models early on, developers can enhance query understanding, decision-making, and overall system performance. This strategic use of LMs not only improves data processing but also lays the foundation for advanced Rag techniques that elevate AI software to new heights of efficiency and capability.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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