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What does Retrieval-augmented generation (RAG) in Quantive StrategyAI mean?
What does Retrieval-augmented generation (RAG) in Quantive StrategyAI mean?
Mariya Dimitrova avatar
Written by Mariya Dimitrova
Updated over a week ago

Retrieval-augmented generation (RAG) is a powerful technology that empowers Quantive StrategyAI to deliver insightful and accurate responses. Here’s a simple breakdown of how RAG works:

  1. Identifying relevant information:

    When you ask a question, RAG begins by meticulously scanning your uploaded documents to locate the most pertinent sections related to your query. This involves analyzing a diverse range of documents and data sources to ensure that the information gathered is comprehensive. By pulling from various documents, RAG provides a multi-faceted perspective that enriches the context of the information presented. This ensures that the responses you receive are not only relevant but also incorporate a wide array of insights from your entire document repository;

  2. Building context:
    After identifying relevant sections, RAG uses them to build context around your question. This step enables Quantive StrategyAI to understand the nuances and specifics of your inquiry, ensuring the response is tailored to your exact needs;

  3. Generating insights:
    With a thorough understanding of the retrieved information and the broader context, Quantive StrategyAI generates a clear and informative response. This process goes beyond simple keyword matching, offering you insights that are accurate, relevant, and actionable.

Through RAG, Quantive StrategyAI enhances its ability to provide valuable insights, making it an indispensable tool for your strategic decision-making.

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