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The automated data processing and API calling also enables FM to deliver updated, tailored answers and perform actual tasks by using proprietary knowledge. This optimization of the model for its intended use allows you to deploy FMs successfully in real applications.
The retriever gathers relevant data and feeds it to the generator, which then uses this context to construct a precise answer. This collaboration bridges the gap between static knowledgemodels and dynamic query resolution, ensuring relevance and fluency.
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