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?" It has made a huge difference.
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 event listener registered in EventServiceProvider."
Architecture decisions become conversations with full context. I described a new module for TOPO Contabil and Claude, having seen the entire Clean Architecture structure, suggested the exact directory layout, interface definitions, and dependency injection bindings that matched every other module. It picked up the pattern across dozens of files without me having to describe it.
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 complete refactoring plan that actually accounts for all the 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 no human reviewer would spot because no human holds 50 files in their head simultaneously. "This new endpoint does not follow the middleware pattern used in all other authenticated routes." That is useful feedback.
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. For quick questions, I still use focused context. But for architecture decisions, major refactors, and complex debugging, the full context approach is worth every token.
This also changed how I structure projects. I am now more disciplined about Clean Architecture because I know 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.
At Yooga we have been experimenting with this for the POS system. The codebase is large, hundreds of files across multiple bounded contexts. Being able to ask "how does a payment flow from the terminal through to the fiscal module" and getting an accurate answer that references actual code is transformative.
I expect to spend less time selecting relevant files and more time letting the AI find them in the full codebase. It is already better at deciding what is relevant than I am.
This also changes the economics of code organization. Monorepos become more attractive when your AI tool can digest the whole thing. The cost of navigating a large codebase drops to zero when your assistant has already read all of it.
I want to keep finding out what else I can do with the full codebase in context.