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    Rohan Paul Describes Resona AI Knowledge Boundary Approach

    Rohan Paul posts that @MeetResona turns one document into multiple agent conversation paths.

    RP
    2 Sources, 28d ago, first seen 28d ago

    TLDR

    Rohan Paul, a Bengaluru-based machine learning engineer, posted that @MeetResona distinguishes knowledge from delivery. The post states the tool treats a document as the knowledge boundary for an agent. This setup lets one fixed body of source material support many possible conversational paths. The creator defines an outcome for the interaction while the agent draws from the material to respond.

    Combined views

    19.3K

    2 Sources, first seen 28d ago

    Combined views

    19.3K

    2 Sources, first seen 28d ago

    8 likes
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    2 comments
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    2 reposts

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    2 comments
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    2 reposts

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    Positive——Negative

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    2 Sources

    @rohanpaul_aiThis AI makes a useful distinction between knowledge and delivery. @MeetResona is treating the document more like the knowledge boundary for an agent, turning one fixed body of knowledge into many possible conversational paths. The creator defines an outcome for the interaction, and the agent uses the source material while adapting how it explains things as the conversation moves toward that outcome. A normal document chatbot is reactive. The user decides what to ask next, so the quality of the session depends heavily on whether they already know the right questions. Here, the document supplies the grounded knowledge, but the agent also has a direction for the conversation.

    2 Sources

    @rohanpaul_aiThis AI makes a useful distinction between knowledge and delivery. @MeetResona is treating the document more like the knowledge boundary for an agent, turning one fixed body of knowledge into many possible conversational paths. The creator defines an outcome for the interaction, and the agent uses the source material while adapting how it explains things as the conversation moves toward that outcome. A normal document chatbot is reactive. The user decides what to ask next, so the quality of the session depends heavily on whether they already know the right questions. Here, the document supplies the grounded knowledge, but the agent also has a direction for the conversation.