# Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains

arXiv: https://arxiv.org/abs/2306.12028 · HTML: https://mlsanity.com/paper/2306.12028/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2023.

## Built on by (most cited first, 24 of 24)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agents](https://mlsanity.com/paper/2405.10467/) (2024)
- [SPADE: Synthesizing Data Quality Assertions for Large Language Model Pipelines](https://mlsanity.com/paper/2401.03038/) (2024)
- [Generative AI for Requirements Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2409.06741/) (2024)
- [Wordflow: Social Prompt Engineering for Large Language Models](https://mlsanity.com/paper/2401.14447/) (2024)
- [Natural Language Outlines for Code: Literate Programming in the LLM Era](https://mlsanity.com/paper/2408.04820/) (2024)
- [Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware](https://mlsanity.com/paper/2402.15943/) (2024)
- [ChainBuddy: An AI Agent System for Generating LLM Pipelines](https://mlsanity.com/paper/2409.13588/) (2024)
- [Prompt Smells: An Omen for Undesirable Generative AI Outputs](https://mlsanity.com/paper/2401.12611/) (2024)
- [Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agents](https://mlsanity.com/paper/2402.16905/) (2024)
- [Can Agents Spontaneously Form a Society? Introducing a Novel Architecture for Generative Multi-Agents to Elicit Social Emergence](https://mlsanity.com/paper/2409.06750/) (2024)
- [Human-AI Experience in Integrated Development Environments: A Systematic Literature Review](https://mlsanity.com/paper/2503.06195/) (2025)
- [Block Circulant Adapter for Large Language Models](https://mlsanity.com/paper/2505.00582/) (2025)
- [Enabling Generative Design Tools with LLM Agents for Mechanical Computation Devices: A Case Study](https://mlsanity.com/paper/2405.17837/) (2024)
- [BioinfoMCP: A Unified Platform Enabling MCP Interfaces in Agentic Bioinformatics](https://mlsanity.com/paper/2510.02139/) (2025)
- [Insert-expansions for Tool-enabled Conversational Agents](https://mlsanity.com/paper/2307.01644/) (2023)
- [VCounselor: A Psychological Intervention Chat Agent Based on a Knowledge-Enhanced Large Language Model](https://mlsanity.com/paper/2403.13553/) (2024)
- [Think-on-Process: Dynamic Process Generation for Collaborative Development of Multi-Agent System](https://mlsanity.com/paper/2409.06568/) (2024)
- [FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementation](https://mlsanity.com/paper/2502.17909/) (2025)
- [AIAP: A No-Code Workflow Builder for Non-Experts with Natural Language and Multi-Agent Collaboration](https://mlsanity.com/paper/2508.02470/) (2025)
- [AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems](https://mlsanity.com/paper/2509.24826/) (2025)
- [Automated Item Neutralization for Non-Cognitive Scales: A Large Language Model Approach to Reducing Social-Desirability Bias](https://mlsanity.com/paper/2509.19314/) (2025)
- [Beyond Prompt-Based Planning: MCP-Native Graph Planning-based Biomedical Agent System](https://mlsanity.com/paper/2606.04494/) (2026)

## Abstract (arXiv)

The emergence of foundation models, such as large language models (LLMs) GPT-4 and text-to-image models DALL-E, has opened up numerous possibilities across various domains. People can now use natural language (i.e. prompts) to communicate with AI to perform tasks. While people can use foundation models through chatbots (e.g., ChatGPT), chat, regardless of the capabilities of the underlying models, is not a production tool for building reusable AI services. APIs like LangChain allow for LLM-based application development but require substantial programming knowledge, thus posing a barrier. To mitigate this, 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, to systematise AI chain engineering methodology. 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, thereby improving the performance and quality of AI chains. With Prompt Sapper, AI chain engineers can compose prompt-based AI services on top of foundation models through chat-based requirement analysis and visual programming. Our user study evaluated and demonstrated the efficiency and correctness of Prompt Sapper.