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In many teams, code is spread across multiple repositories, and each repo contains only a fragment of the overall context. This makes it hard for AI assistants to understand a system end to end. Monorepos centralize code and signals, but once a monorepo grows large, the key challenge becomes how to represent and access that knowledge efficiently so AI can use it. <br><br>In this talk, Qun Lin will share a practical approach to make AI truly monorepo aware by building a monorepo indexing layer and connecting it to the model through MCP and composable skills. We will look at what to index in a large monorepo, how to organize information at the package and workspace level, and how to expose these capabilities as tools that AI can call. The goal is to let AI navigate the codebase with stronger context, answer questions more accurately, and generate changes that fit the monorepo structure.
