# Single Conversation Methodology: A Human-Centered Protocol for AI-Assisted Software Development

arXiv: https://arxiv.org/abs/2507.12665 · HTML: https://mlsanity.com/paper/2507.12665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [A Survey on Large Language Models for Software Engineering](https://mlsanity.com/paper/2312.15223/) (2023)
- [Investigating and Designing for Trust in AI-powered Code Generation Tools](https://mlsanity.com/paper/2305.11248/) (2023)
- [Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice](https://mlsanity.com/paper/2404.14901/) (2024)
- [Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning](https://mlsanity.com/paper/2410.08922/) (2024)
- [LLMs: A Game-Changer for Software Engineers?](https://mlsanity.com/paper/2411.00932/) (2024)
- [Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach](https://mlsanity.com/paper/2412.00726/) (2024)
- [LLMs' Reshaping of People, Processes, Products, and Society in Software Development: A Comprehensive Exploration with Early Adopters](https://mlsanity.com/paper/2503.05012/) (2025)
- [The Impact of Generative AI Coding Assistants on Developers Who Are Visually Impaired](https://mlsanity.com/paper/2503.16491/) (2025)
- [A Case Study in Engineering a Conversational Programming Assistant's Persona](https://mlsanity.com/paper/2301.10016/) (2023)
- [Testing the Effect of Code Documentation on Large Language Model Code Understanding](https://mlsanity.com/paper/2404.03114/) (2024)
- [Ethics of Software Programming with Generative AI: Is Programming without Generative AI always radical?](https://mlsanity.com/paper/2408.10554/) (2024)

## Abstract (arXiv)

We propose the Single Conversation Methodology (SCM), a novel and pragmatic approach to software development using large language models (LLMs). In contrast to ad hoc interactions with generative AI, SCM emphasizes a structured and persistent development dialogue, where all stages of a project - from requirements to architecture and implementation - unfold within a single, long-context conversation. The methodology is grounded on principles of cognitive clarity, traceability, modularity, and documentation. We define its phases, best practices, and philosophical stance, while arguing that SCM offers a necessary correction to the passive reliance on LLMs prevalent in current practices. We aim to reassert the active role of the developer as architect and supervisor of the intelligent tool.