Prompt Design and Engineering: Introduction and Advanced Methods

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1. Très proche Pas pertinent

   A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

   Jules White, Quchen Fu, Sam Hays et al. · 2023 · Génie logiciel · Résumé IA disponible

   Résumé scientifique

   Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM. This paper describes a catalog of prompt engineering techniques presented in pattern form that have been applied to solve common problems when conversing with LLMs. Prompt patterns are a knowledge transfer method analogous to software patterns since they provide reusable solutions to common problems faced in a …

2. Proche Pas pertinent

   Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks

   E. G. Santana Jr, Gabriel Benjamin, Melissa Araujo et al. · 2025 · Génie logiciel · Pas encore résumé

   Résumé scientifique

   A growing variety of prompt engineering techniques has been proposed for Large Language Models (LLMs), yet systematic evaluation of each technique on individual software engineering (SE) tasks remains underexplored. In this study, we present a systematic evaluation of 14 established prompt techniques across 10 SE tasks using four LLM models. As identified in the prior literature, the selected prompting techniques span six core dimensions (Zero-Shot, Few-Shot, Thought Generation, Ensembling, Self-Criticism, and Decomposition). They are evaluated on tasks such as code generation, bug fixing, and code-oriented question answering, to name a few. Our results show which prompting techniques are mo…

3. Proche Pas pertinent

   ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

   Jules White, Sam Hays, Quchen Fu et al. · 2023 · Génie logiciel · Résumé IA disponible

   Résumé scientifique

   This paper presents prompt design techniques for software engineering, in the form of patterns, to solve common problems when using large language models (LLMs), such as ChatGPT to automate common software engineering activities, such as ensuring code is decoupled from third-party libraries and simulating a web application API before it is implemented. This paper provides two contributions to research on using LLMs for software engineering. First, it provides a catalog of patterns for software engineering that classifies patterns according to the types of problems they solve. Second, it explores several prompt patterns that have been applied to improve requirements elicitation, rapid prototy…

4. Proche Pas pertinent

   Requirements Engineering using Generative AI: Prompts and Prompting Patterns

   Krishna Ronanki, Beatriz Cabrero-Daniel, Jennifer Horkoff et al. · 2023 · Génie logiciel · Résumé IA disponible

   Résumé scientifique

   [Context]: Companies are increasingly recognizing the importance of automating Requirements Engineering (RE) tasks due to their resource-intensive nature. The advent of GenAI has made these tasks more amenable to automation, thanks to its ability to understand and interpret context effectively. [Problem]: However, in the context of GenAI, prompt engineering is a critical factor for success. Despite this, we currently lack tools and methods to systematically assess and determine the most effective prompt patterns to employ for a particular RE task. [Method]: Two tasks related to requirements, specifically requirement classification and tracing, were automated using the GPT-3.5 turbo API. The …

5. Proche Pas pertinent

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

   Zhenchang Xing, Qing Huang, Yu Cheng et al. · 2023 · Génie logiciel · Résumé IA disponible

   Résumé scientifique

   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 pot…

6. Proche Pas pertinent

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

   Yu Cheng, Jieshan Chen, Qing Huang et al. · 2023 · Génie logiciel · Résumé IA disponible

   Résumé scientifique

   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…

7. Apparenté Pas pertinent

   Reliability of Large Language Models for Design Synthesis: An Empirical Study of Variance, Prompt Sensitivity, and Method Scaffolding

   Rabia Iftikhar, Andreas Rausch · 2026 · International Conference on Software Architecture 2026 · Résumé IA disponible

   Résumé scientifique

   Large Language Models (LLMs) are increasingly applied to automate software engineering tasks, including the generation of UML class diagrams from natural language descriptions. While prior work demonstrates that LLMs can produce syntactically valid diagrams, syntactic correctness alone does not guarantee meaningful design. This study investigates whether LLMs can move beyond diagram translation to perform design synthesis, and how reliably they maintain design-oriented reasoning under variation. We introduce a preference-based few-shot prompting approach that biases LLM outputs toward designs satisfying object-oriented principles and pattern-consistent structures. Two design-intent benchmark…

8. Apparenté Pas pertinent

   Communicative Agents for Software Development

   Chen Qian, Xin Cong, Wei Liu et al. · 2023 · Génie logiciel · Résumé IA disponible

   Résumé scientifique

   Software engineering is a domain characterized by intricate decision-making processes, often relying on nuanced intuition and consultation. Recent advancements in deep learning have started to revolutionize software engineering practices through elaborate designs implemented at various stages of software development. In this paper, we present an innovative paradigm that leverages large language models (LLMs) throughout the entire software development process, streamlining and unifying key processes through natural language communication, thereby eliminating the need for specialized models at each phase. At the core of this paradigm lies ChatDev, a virtual chat-powered software development co…