everystack-phase-context

Providing AI context for a monorepo involves documenting the project structure, established patterns, and development phase progress. This ensures AI models understand architectural conventions and maintain context without rediscovering existing files during interactions.

AI assistants ignore coding conventions when they lack explicit context about the codebase state. Documenting architectural decisions, key files, and established patterns like conventional commits and RLS policies ensures the AI adheres to project standards during development tasks.

Documenting codebase state for AI models requires detailing the monorepo structure, dependencies, and development phase progress. This contextual information informs the AI about where to place new files, which utilities to reuse, and what architectural conventions to follow.

Yes, you can use codebase context to inform AI about infrastructure, database, auth, and observability patterns. Detailing these architectural conventions ensures the AI model maintains accurate project context and adheres to established standards across all runtime services.

Providing project structure context prevents AI from duplicating existing utilities by informing it of available modules and dependencies. This awareness allows the AI to reuse established code and adhere to the monorepo's architectural conventions during feature implementation.

Maintaining AI context across development phases requires documenting the progress of infrastructure, database, auth, and observability layers. This ongoing documentation ensures the AI model understands the current build state and architectural decisions for subsequent tasks.