Chapter 13: Key Takeaways

Working with Multiple Files and Large Codebases

Use this summary card for quick review of the essential concepts from Chapter 13.

  • The fundamental challenge of multi-file vibe coding is that AI assistants cannot see your entire codebase. They only know what you explicitly provide in the conversation. The quality of AI-generated code is directly proportional to the quality of the context you provide about the rest of the project.

  • Repository maps are your most powerful orientation tool. A structured summary showing each file's path, line count, key classes/functions, and dependencies gives the AI a bird's-eye view of the project that a simple directory tree cannot provide.

  • Default to interface-first context. When the AI needs to interact with other modules, provide only the public signatures (class definitions, method signatures with type hints, docstrings) rather than full implementations. This is the most token-efficient strategy and is sufficient for the vast majority of cross-file integration work.

  • Include full files only when modifying them, debugging, or when implementation details matter. Reserve full file inclusion for situations where the interface alone is insufficient: editing existing code, investigating bugs, or working with complex internal logic.

  • Use holistic generation for scaffolding and tightly coupled components; use file-by-file generation for complex individual modules. The hybrid approach gives you the consistency benefits of generating related files together with the quality benefits of focused single-file generation.

  • Establish conventions before generating code, and re-anchor them throughout the session. Create a compact style guide covering naming, patterns, error handling, and import conventions. Include it (or a condensed version) in every prompt to prevent convention drift over long sessions.

  • Use a consistency reference file alongside your style guide. Showing the AI one concrete example of a well-written file in your project is more effective than describing conventions abstractly. The AI pattern-matches against the reference and replicates its style.

  • Provide import maps and dependency rules to prevent incorrect imports and circular dependencies. Explicitly list what each module exports, the correct import paths, and which layers may import from which. This prevents architectural violations and import errors that are difficult to debug.

  • Scope monorepo work to individual packages. Never try to work with an entire monorepo at once. Focus each AI session on one package, providing interfaces from shared libraries and dependencies. For cross-package changes, modify upstream packages first, then update downstream packages in subsequent sessions.

  • AI excels at code navigation, not just generation. Use AI to understand unfamiliar codebases, trace data flows, analyze dependency structures, and identify where to make changes. This is one of the highest-value applications of AI in professional development.

  • Use the tiered context strategy for enterprise codebases. Layer your context from always-included basics (project name, architecture style) through session-level summaries to task-specific file contents and on-demand details. This scales the techniques in this chapter to codebases of any size.

  • Maintain a living context document for your project. A markdown file summarizing your architecture, conventions, module purposes, and key decisions can be pasted at the start of any new AI conversation. Keep it updated as the project evolves, and consider checking it into version control.

  • Run consistency checks after every batch of generated files. Automated checking for naming convention violations, missing docstrings, import style inconsistencies, and type hint coverage catches problems early before they propagate through the codebase.

These takeaways connect directly to Chapter 9 (Context Management) and build the foundation for Chapter 14 (When AI Gets It Wrong). The context-providing strategies here are essential for every subsequent chapter that involves building real software.