๐—ง๐—ต๐—ถ๐˜€ ๐—œ๐˜€ ๐—ช๐—ต๐˜† ๐—ฌ๐—ผ๐—Ž๐—ฟ ๐—”๐—œ ๐—”๐—ฎ๐—ป'๐˜ ๐—จ๐—ป๐—ฑ๐—ฒ๐—ฟ๐˜€๐˜๐—ฎ๐—ป๐—ฑ ๐—Ÿ๐—ฎ๐—ฟ๐—ด๐—ฒ ๐—ฅ๐—ฒ๐—ฝ๐—ผ๐˜€๐—ถ๐˜๐—ผ๐—ฟ๐—ถ๐—ฒ๐˜€ AI coding assistants are impressive. They can:

Most people think AI gives wrong answers because it's not smart enough. But that's usually not the problem. The real issue is lack of context. AI can understand code, but understanding an entire software system is a different challenge.

Imagine a repository with thousands of files, hundreds of APIs, and multiple services. Now ask: "How does user onboarding work?" The answer involves many parts of the system. AI tools find relevant files and code snippets, but finding something is not the same as understanding it.

Large systems are built from relationships. Examples:

That's why you may get answers that look reasonable but are completely wrong for your project. In large systems, the hardest task is often not writing code, but understanding the existing architecture, service dependencies, and data flow.

Concepts like repository memory, knowledge graphs, and architectural maps are becoming more important. They treat the repository as a connected system, not isolated files. Now AI can understand relationships, dependencies, and data flow, not just raw code.

Tools that build repository memory help solve this problem. They transform files into knowledge. The next challenge for AI is understanding systems, not just generating code. Because real-world software is about architecture, relationships, context, and decisions, not just code.

Source: https://dev.to/md_mijanur_molla/why-your-ai-assistant-gets-lost-in-large-repositories-3c9i Optional learning community: https://t.me/GyaanSetuAi