Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services

• URL: http://arxiv.org/abs/2306.02230v1
• Date: Sun, 4 Jun 2023 01:47:42 GMT
• Title: Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services
• Authors: Zhenchang Xing, Qing Huang, Yu Cheng, Liming Zhu, Qinghua Lu, Xiwei Xu
• Abstract summary: Prompt Sapper is committed to support the development of AI-native services by AI chain engineering. It creates a large language model (LLM) empowered software engineering infrastructure for authoring AI chains through human-AI collaborative intelligence. This article will introduce the R&D motivation behind Prompt Sapper, along with its corresponding AI chain engineering methodology and technical practices.
• Score: 37.05145017386908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models, such as GPT-4, DALL-E have brought unprecedented AI "operating system" effect and new forms of human-AI interaction, sparking a wave of innovation in AI-native services, where natural language prompts serve as executable "code" directly (prompt as executable code), eliminating the need for programming language as an intermediary and opening up the door to personal AI. Prompt Sapper has emerged in response, committed to support the development of AI-native services by AI chain engineering. It creates a large language model (LLM) empowered software engineering infrastructure for authoring AI chains through human-AI collaborative intelligence, unleashing the AI innovation potential of every individual, and forging a future where everyone can be a master of AI innovation. This article will introduce the R\&D motivation behind Prompt Sapper, along with its corresponding AI chain engineering methodology and technical practices.

Related papers

• A call for embodied AI [1.7544885995294304]
  We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development.
  arXiv  Detail & Related papers  (2024-02-06T09:11:20Z)
• Exploration with Principles for Diverse AI Supervision [88.61687950039662]
  Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
  arXiv  Detail & Related papers  (2023-10-13T07:03:39Z)
• Large Language Models Empowered Autonomous Edge AI for Connected Intelligence [51.269276328087855]
  Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence. This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
  arXiv  Detail & Related papers  (2023-07-06T05:16:55Z)
• Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains [31.080896878139402]
  We propose the concept of AI chain and introduce the best principles and practices that have been accumulated in software engineering for decades into AI chain engineering. We also develop a no-code integrated development environment, Prompt Sapper, which embodies these AI chain engineering principles and patterns naturally in the process of building AI chains.
  arXiv  Detail & Related papers  (2023-06-21T05:31:00Z)
• End-User Development for Artificial Intelligence: A Systematic Literature Review [2.347942013388615]
  End-User Development (EUD) can allow people to create, customize, or adapt AI-based systems to their own needs. This paper presents a literature review that aims to shed the light on the current landscape of EUD for AI systems.
  arXiv  Detail & Related papers  (2023-04-14T09:57:36Z)
• HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face [85.25054021362232]
  Large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning. LLMs could act as a controller to manage existing AI models to solve complicated AI tasks. We present HuggingGPT, an LLM-powered agent that connects various AI models in machine learning communities.
  arXiv  Detail & Related papers  (2023-03-30T17:48:28Z)
• AI-in-the-Loop -- The impact of HMI in AI-based Application [0.0]
  We introduce the AI-in-the-loop concept that combines AI's and humans' strengths. By enabling HMI during an AI uses inference, we will introduce the AI-in-the-loop concept that combines AI's and humans' strengths.
  arXiv  Detail & Related papers  (2023-03-21T00:04:33Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Time for AI (Ethics) Maturity Model Is Now [15.870654219935972]
  This paper argues that AI software is still software and needs to be approached from the software development perspective. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system.
  arXiv  Detail & Related papers  (2021-01-29T17:37:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.