related-file-planning

Planning code changes requires discovering entry points, services, and tests to build a repository file map. This grounds implementation in actual codebase context, preventing speculative edits by summarizing current behavior and highlighting risks before modifying code.

Preventing speculative code changes during debugging involves mapping relevant files and reading context before acting. This evidence-based workflow identifies the exact repository components causing issues, ensuring fixes are grounded in actual codebase behavior rather than guesswork.

Mapping repository context for an architecture review involves scoping the codebase to find entry points, UI components, and configuration files. This process builds an evidence-based file map that summarizes current behavior and highlights structural gaps or risks.

Evidence-based planning works for full-stack refactoring tasks by scoping the repository to identify frontend and backend files. It maps dependencies and test coverage, ensuring structural changes are validated against actual codebase context before implementation begins.

Reading repository files before generating AI coding prompts ensures the prompt contains accurate codebase context. This file discovery and context mapping step prevents speculative AI outputs by grounding the prompt design in actual entry points, services, and tests.

You can use this workflow for test validation in a backend codebase by discovering relevant test files and mapping them to services. This test-aware approach ensures recommendations highlight coverage gaps and validate behavior before code modification.