RAG Pipelines refer to a technology that integrates retrieval-augmented generation (RAG) methods, combining the strengths of retrieval-based and generative models to improve efficiency and accuracy in information processing.
The technology addresses the limitations of purely generative models in terms of accuracy and efficiency, as well as the limitations of pure retrieval systems in generating new content.
These pipelines work by first retrieving relevant documents or passages from a database using a retrieval model. Then, a generative model is used to refine and expand this retrieved content into more detailed or coherent responses. This approach leverages the speed and precision of retrieval models with the creativity and context understanding of generative models.
Manufacturing involves developing and training both retrieval and generative models. This requires significant computational resources and expertise in natural language processing (NLP) techniques.
The build process includes data collection, model selection, training, fine-tuning, and validation steps. The pipeline is then tested for performance metrics such as recall, precision, and generation quality before deployment.
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