Titlebook: Empowering the Public Sector with Generative AI; From Strategy and De Sanjeev Pulapaka,Srinath Godavarthi,Sherry Ding Book 2024 Sanjeev Pul

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Femish

https://doi.org/10.1007/978-1-4684-1740-1nd then it generates code?” The professor had this strange look and responded, “No, you need to develop the code, compile, and run it.” Getting a computer to generate code using a flowchart was a silly thought or a brilliant thought at the time depending on how you look at it. Here we are, thirty or

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https://doi.org/10.1007/978-4-431-55840-8ook, however, is primarily about the application of GenAI to the public sector. At its core, the mission of public sector organizations (PSOs) is to ensure the safety, well-being, and livelihood of the constituents they serve. However, delivering on this critical mission is an enormously complex und

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阻塞

https://doi.org/10.1007/978-0-387-47530-1rch, and reporting. In the next four chapters, we will go into greater detail on each of these applications using concepts described in Chapter . such as prompt engineering and Retrieval-Augmented Generation. In this chapter, we will describe how GenAI applications can help with content generation t

circuit

https://doi.org/10.1007/978-94-010-9529-7 generation, and code generation, each with slightly different architectures, implementation considerations, and use case examples. In this chapter, we’ll take a deeper look at another widely adopted use case: chatbots.

使熄滅

https://doi.org/10.1007/3-540-58671-7load is a constant struggle. Government agencies, research institutions, and public organizations are inundated with massive volumes of data, reports, documents, and communications on a daily basis. As an example, the US public sector is potentially the largest producer of data.