# Context Before Code: An Experience Report on Vibe Coding in Practice

arXiv: https://arxiv.org/abs/2603.11073 · HTML: https://mlsanity.com/paper/2603.11073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RAG for code, one of 15 directions in Retrieval-augmented generation.
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Placed in

- Retrieval-augmented generation / [RAG for code](https://mlsanity.com/rag/rag-for-code/)

## Builds on (most cited first, 6 of 6)

- [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024)
- [Vibe coding: programming through conversation with artificial intelligence](https://mlsanity.com/paper/2506.23253/) (2025)
- [Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation](https://mlsanity.com/paper/2506.20869/) (2025)
- [Vibe Coding in Practice: Flow, Technical Debt, and Guidelines for Sustainable Use](https://mlsanity.com/paper/2512.11922/) (2025)
- [Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Education](https://mlsanity.com/paper/2512.23982/) (2025)
- [Beyond the Commit: Developer Perspectives on Productivity with AI Coding Assistants](https://mlsanity.com/paper/2602.03593/) (2026)

## Abstract (arXiv)

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.