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

arXiv: https://arxiv.org/abs/2306.02230 · HTML: https://mlsanity.com/paper/2306.02230/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2023.

## Built on by (most cited first, 5 of 5)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification](https://mlsanity.com/paper/2307.02192/) (2023)
- [Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code](https://mlsanity.com/paper/2312.14856/) (2023)
- [A Taxonomy of Prompt Defects in LLM Systems](https://mlsanity.com/paper/2509.14404/) (2025)
- [Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale](https://mlsanity.com/paper/2511.08475/) (2025)

## Abstract (arXiv)

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.