Retrieval-Augmented Generation (RAG) pipelines are a type of artificial intelligence technology that combines the strengths of retrieval-based and generative models to extract, summarize, and generate information from large datasets.
Addressing the limitations of pure generative models in terms of accuracy and relevance by integrating them with retrieval models to ensure that generated content is grounded in factual information from reliable sources.
RAG pipelines work by first retrieving relevant documents or passages from a knowledge base using a retrieval model. Then, a generative model is used to process these retrieved documents and produce context-aware responses or summaries.
The manufacturing process involves developing and training both retrieval and generation models, integrating them into a pipeline architecture, and continuously fine-tuning these models based on feedback and new data.
Models are built using large-scale datasets and advanced machine learning techniques. The process includes data preprocessing, model training, evaluation, and deployment.
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