feat: add implementation strategy for existing docs - AI Code Generation Context

patterns/1-initial/ai-code-generation-context.md

@@ -103,7 +103,9 @@ Create an `innersource-ai/` folder in the repository root containing:
 
 **Context Efficiency**: Start with core documentation files (~1000 words of context) to balance context value with AI tool costs. Expand strategically based on measured impact on review cycles and code quality.
 
-**Naming Convention**: The suggested file and folder names follow industry common practices. However, codebase owners may choose alternative names that are more discoverable and relatable to their specific project or codebase. Any chosen naming convention should be clearly documented and communicated to contributors through proper documentation. Should files like [AGENTS.md](https://agents.md) become standard in the future, the naming conventions in this pattern might be adapted accordingly.
+**Naming Convention**: The suggested file and folder names follow industry common practices. However, codebase owners may choose alternative names that are more discoverable and relatable to their specific project or codebase. Any chosen naming convention should be clearly documented and communicated to contributors through proper documentation. Should files like [AGENTS.md](https://agents.md) and `.aiignore` become standard in the future, the naming conventions in this pattern might be adapted accordingly.
+
+**Handling Existing Documentation**: If files like `ARCHITECTURE.md` already exist, the pragmatic approach is to keep them in their current location and add lightweight reference files in `innersource-ai` that point to them. When the architecture docs, style guides, or other materials are in Confluence or similar external systems, the `innersource-ai` folder becomes a crucial bridge between the codebase and external knowledge. This avoids duplication and keeps the folder consistent. For projects that want tighter integration, code owners could choose to reorganize and consolidate content under `innersource-ai`, but that requires more effort. The approach is flexible enough to support either approach—or even a hybrid—depending on what works best for the repository.
 
 ### Usage Patterns