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.

Artificial intelligence is making the process of designing, developing, and deploying software faster, better, and cheaper. It's not that programmers are being replaced by robots--rather, AI-powered tools are making project managers, business analysts, software coders, and testers more productive and more effective, enabling them to produce higher-quality software faster at lower cost. AI may become a key factor in meeting rising demand for custom software. Developing and deploying custom software is a critical element of how many companies innovate,5 with top-performing organizations developing many of their most important software solutions in-house.6 And the market for custom software development services is large: around US$47 billion in 2018 and climbing.7

Being a developer is more demanding than ever. The repetitive tasks that make up so much of software development can be time-consuming and error-prone. Talent is in short supply, teams are overworked, and many businesses can't keep up with both increasingly complex existing code and the growing market for new application development. For AI enthusiasts, speculating about how artificial intelligence can improve software development is exciting. Will AI help create prototypes in days, instead of months or years?

Today's continuous integration bottlenecks need AI for code The impact artificial intelligence could have on software development is ripe for speculation. Will AI help create prototypes in days, instead of months or years? Will it teach human developers how to code better? AI research is broad and code is flexible, so it's hard to imagine what software development will look like when intelligent programs can help us interact with code. What many developers and tech managers don't realise is that AI has made huge leaps in its usefulness for development teams in the last several years.