Vibe Coding's performance with large, multi-module repositories is a mixed bag, presenting both significant challenges and potential solutions. The primary obstacle is the AI agent's limited context window, which is the amount of code it can "see" and process at one time. In a massive codebase, the AI might struggle to grasp all the intricate dependencies, internal APIs, and data flow between different modules, leading to code that is syntactically correct but contextually flawed, such as calling functions that don't exist or using data models incorrectly. This can result in hallucinations where the AI generates plausible-looking but inaccurate code references.
To overcome these limitations, specific strategies and tools are essential. The most effective approach is to enhance the AI's context using techniques like Retrieval-Augmented Generation (RAG). Tools like Cursor's @codebase feature or the "Context Enhanced Mode (CEM)" described in Vibe Coding surveys are designed for this purpose. They work by creating a searchable index of your repository. When you ask the AI to implement a feature, it doesn't rely solely on its pre-trained knowledge; instead, it first retrieves and reads the most relevant code snippets, documentation, and configuration files from your project. This allows it to generate code that aligns with your project's specific patterns and dependencies. Furthermore, breaking down large tasks is crucial. Instead of prompting "add a new payment module," a more effective method is to use a "Plan Mode," where you first collaborate with the AI to create a step-by-step implementation plan, then tackle each smaller, self-contained step one by one.
For teams working on complex systems, a shift in process is often necessary for Vibe Coding to be successful. This involves adopting a more disciplined, "AI-first engineering" approach. This means providing the AI with high-level and technical specifications before coding begins, feeding it with repository maps and API schemas, and enforcing rigorous review gates. In this workflow, an AI agent might act as the first reviewer of code before a human engineer conducts the final review. By combining context-enhancement technologies like RAG, a methodical approach to task decomposition, and a structured review process, Vibe Coding can be scaled to manage large, multi-module repositories, though it requires more upfront setup and process refinement compared to working on smaller, green-field projects.