RAG Pipelines (Retrieval-Augmented Generation)

RAG Pipelines (Retrieval-Augmented Generation) combine the strengths of retrieval-based systems with generative models to produce coherent and contextually accurate outputs.

Addressing the limitations of purely generative models in producing coherent and contextually accurate responses by integrating retrieval capabilities.

RAG pipelines first retrieve relevant information from a database or knowledge base, then use this retrieved data as input for a generative model to generate a response. This two-step process ensures that the generated content is both relevant and contextually accurate.

• retrieval models
• generative models
• knowledge bases

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Develop and train a retrieval model to efficiently search for relevant information, then integrate this with a generative model that can produce coherent text based on the retrieved data. Fine-tuning both models together is crucial for optimal performance.

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Retrieval-Augmented Generation (RAG)

A technology that combines retrieval of relevant information with generative models to produce coherent and contextually accurate outputs.

Generative Models

Machine learning models designed to generate new data instances similar to a training dataset, often used for tasks like text generation or image synthesis.

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