RAG pipelines, or Retrieval-Augmented Generation pipelines, integrate retrieval-based models with generative AI to enhance the accuracy and contextual richness of responses in knowledge management systems.
Improving the accuracy and contextual richness of responses in complex information retrieval tasks, thereby enhancing user satisfaction and efficiency.
These pipelines first retrieve relevant documents or passages from a database or corpus using a retrieval model. Then, a generative model is used to generate a response that incorporates the retrieved information while adding contextually rich details.
The manufacturing process involves developing and training both retrieval-based and generative models, integrating them into a pipeline, and fine-tuning to ensure optimal performance.
Models are built using large datasets for training. The retrieval model is first trained on a corpus of documents to understand the context and structure. The generative model is then trained to generate coherent responses based on the retrieved information.
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