A few weeks with Claude's 1M token context window have changed how I work through software architecture decisions.
Before: I would carefully select which files to share with the AI. "Here is the service file, here is the repository interface, here is the relevant test." I was the curator, deciding what context mattered. Half the time I would miss something important and the AI would give me advice that conflicted with a constraint it did not know about.
Now I feed it the entire codebase, including models, services, repositories, controllers, tests, configs, and migrations. Then I ask "given all of this, how should I implement feature X?"
When Claude can see your entire codebase, it catches things you forgot existed. "I notice you have a similar pattern in OrderService, should we follow the same approach?" "Your BaseRepository already handles soft deletes, you do not need to implement it again." "This would conflict with the interceptor already registered globally in AppModule."
I can talk through architecture decisions with full context. In a Clean Architecture codebase, when I sketch where a new module should go, Claude can point to the directory layout, interface shapes and dependency injection bindings the existing modules already use, because it has read them. The decision stays mine, and it gets made with the whole pattern in view.
For refactoring, I can now say "refactor the payment processing flow to use the strategy pattern" and Claude sees every file that touches payments. That includes the controllers, services, tests, and event listeners. It produces a refactoring plan that accounts for those dependencies. Before, I would refactor one layer and discover three others that broke.
For code review, I paste the entire PR diff plus the surrounding codebase context. Claude catches inconsistencies that are easy to miss in review, because no one holds 50 files in their head at once. "This new endpoint does not follow the middleware pattern used in all other authenticated routes."
When debugging, instead of "here is the error and here is the file it happens in," I can say "here is the error" and let Claude trace through the entire call stack itself. It finds the root cause three layers deep in a file I would not have thought to check.
For documentation, Claude can generate accurate architecture documentation from the code itself. The generated docs reference the class names and method signatures in the project and explain how they relate.
The tradeoff is cost and speed. Sending 500K tokens of context is not cheap and the response time increases. I have learned to be strategic about it.
I have also become more disciplined about Clean Architecture, knowing an AI will be reading the entire codebase regularly. Clear boundaries and consistent naming make the patterns easier for both people and machines to follow.
For the big questions, I spend less time picking files and more time letting the AI find the relevant ones in the full codebase.
This also changes the math on how to organize code. Monorepos become more attractive when your AI tool can digest the whole thing, because navigating a large codebase costs much less when the assistant has already read all of it.
My rule now: focused context for quick questions and changes that stay in one file; the full codebase for architecture decisions, refactors that cross layers, and bugs I cannot place.