Case Study: Automating USAID PerformancePlan and Report (PPR)

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    DevelopMetrics
    November 14th, 2023
    USAID PPR

    Introduction

    Accepting Administrator Power’s challenge to streamline processes and cut red tape, the USAID Tajikistan Mission pioneered the use of Artificial Intelligence to automate the generation of PPR narratives, reclaiming roughly 1,250 annual staff hours and mitigating the need for extended work days to adhere to critical deadlines. To achieve this, the Mission leveraged DevelopMetrics’ advanced technology, known as the Development Evidence Large Learning Model (DEELM), to automate the generation of PPR narratives.

    Project Scope & Methodology

    The scope of the pilot project was to leverage machine learning technology to draft two PPR narratives. In the initial phase, DEELM underwent extensive training based on policy documents, annual activity reports, narrative guidance, and all USAID documents in the Development Clearing House. The aim was to acquaint the model with the intricate details, context, and content of USAID’s programs in the specified areas. Drawing on the knowledge acquired during the training phase, DEELM autonomously generated machine-authored narratives in line with the narrative instruction provided. Lastly, a thorough evaluation was conducted, comparing the machine-generated narrative to the traditionally prepared narratives. This comprehensive review assessed coherence, accuracy, and alignment with the established reporting guidelines to ensure the quality standards expected in the PPR.

    Keywords:
    USAID Performance Plan and Report (PPR)
    Automating USAID reporting
    Artificial Intelligence in development reporting
    AI-generated narratives
    Streamlining USAID processes
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    Born from a collaboration between diplomats and data scientists, DevelopMetrics harnesses its proprietary machine learning algorithm, Development Evidence Large Language Model (DELLM), to enhance the efficacy and impact of international development through comprehensive evidence analysis.

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