Context Before Code: An Experience Report on Vibe Coding in Practice
This addresses the challenge of using code-generating tools in production for software developers, highlighting incremental insights into limitations and required architectural oversight.
The paper reports on applying conversational 'vibe coding' with contextual prompting and architectural constraints to build a multi-project agent learning platform and an academic RAG system, finding that it accelerated scaffolding and integration but often under-specified isolation and infrastructure aspects, shifting effort toward constraint specification and auditing.
Code-generating tools are increasingly used in software development, yet experience reports on conversational "vibe coding" under production constraints remain limited. This paper presents an experience report from a small full-stack team that applied contextual prompting and explicit architectural constraints to build (i) a multi-project agent learning platform designed for sustained, production-oriented use and (ii) an academic retrieval-augmented generation system. The agent platform supports multiple isolated projects, each with structured memory and background processing, thereby enforcing project-level isolation. The RAG system provides citation-grounded answers, role-based access control, and evaluation tracking. Across both systems, vibe coding accelerated scaffolding and integration. However, the generated code often under-specified isolation rules and infrastructure constraints when these were not explicitly defined. Consequently, aspects such as multi-tenancy, access control, memory policies, and asynchronous processing required deliberate architectural design and verification. We observe a shift in engineering effort from boilerplate implementation toward constraint specification and enforcement auditing. We also identify recurring architectural "non-delegation zones" where conversational code generation remains insufficient for production reliability.