🤖 AI Summary
Current AI coding agents often produce code requiring extensive debugging due to insufficient contextual understanding, thereby diminishing development efficiency. This work proposes a three-phase preparation methodology inspired by the culinary concept of “mise en place”—termed MEP—comprising context anchoring, collaborative specification formulation, and task decomposition, which enhances agent coding performance through structured contextualization. The study introduces “contextual fluency” as a novel developer competency, integrating backward design principles and theories of tacit knowledge externalization. It employs structured documentation, human–AI collaborative dialogues, and dependency-aware task logging to operationalize this approach. In a hackathon setting, just two hours of preparatory work enabled multiple AI agents to concurrently and effectively construct a complete educational platform, substantially reducing the overall development cycle.
📝 Abstract
The rapid adoption of AI coding agents has produced a dominant workflow pattern -- often called "vibe coding" -- that prioritizes speed of implementation over deliberate preparation. We argue that this approach creates a systematic alignment problem: agents that lack sufficient context produce code requiring extensive debugging and refactoring, consuming substantial development time. Drawing on the culinary concept of mise en place (everything in its place; abbreviated MEP), we propose a three-phase preparation methodology for agentic coding: (1) contextual grounding, where domain expertise and tacit knowledge are externalized into structured documents; (2) collaborative specification, where human-agent dialogue produces detailed design artifacts; and (3) task decomposition, where specifications are converted into structured, dependency-aware task records. We report on the application of MEP during a competitive hackathon, where roughly two hours of preparation enabled a rapid parallel implementation of a full-stack educational platform by concurrent AI agents. We introduce the concept of context fluency as an emerging developer skill -- the ability to create rich, structured context that agents can act on -- and connect it to established frameworks in backward design and tacit knowledge externalization. We conclude with a research agenda for empirically validating preparation-phase methodologies in AI-assisted software development.