Add a Feature to an Existing Codebase with AI — Without Starting Over
A repository-to-feature workflow for developers: give AI the right context, preserve your architecture, review a bounded diff, and verify the actual behavior.
Most software work begins with an existing application, not an empty folder. There are conventions, dependencies, tests, deployment settings, and decisions that may not be documented. Extending that application is a different task from generating a new demo. To use AI effectively, give it the project's contract, choose a bounded change, and verify that the implementation fits. Here is a practical workflow using CSV export from an orders page as an illustrative feature.
Choose a small feature with a clear contract
Start with an instruction such as: Add CSV export to the existing orders page. Preserve the current filters and authorization rules. Use the existing API patterns and do not add a UI library. Define which fields belong in the export and how empty results should behave. This is easier to evaluate than improve the dashboard because the expected outcome and the boundaries are explicit.
Connect the repository and locate the real entry point
Multos supports connecting existing repositories through GitHub, GitLab, or Bitbucket. Begin on a dedicated branch or an isolated copy and preserve unrelated work. Identify the page, API handler, types, and services involved in the feature. Include similar implementations and relevant tests as context. Repository access is only the starting point; the useful context is the code that actually governs the behavior being changed.
Ask for an implementation plan that respects the architecture
For a change spanning several modules, ask which files and interfaces need to change, whether a dependency is necessary, and how the result will be verified. Request the smallest coherent implementation. Do not let a straightforward export feature become a rewrite of the orders service. Call out existing naming, validation, logging, and error-handling conventions that should remain consistent.
Keep security rules on the server
The export must respect the same access rules as the page, even if someone calls the endpoint directly. Define whether it exports the visible page or all authorized filtered results. Handle spreadsheet formula interpretation safely when exporting untrusted cell values, and encode commas, quotes, and line breaks correctly. These are feature requirements, not optional polish. Review the generated implementation against them rather than assuming a visible download button proves correctness.
Review the diff before accepting the preview
Inspect unrelated edits, new dependencies, changed public interfaces, and error handling that could hide failures. Check that the AI has not replaced established patterns with a second architecture. Confirm that secrets and private records are not exposed. A screenshot can show the new button; only source review and behavior checks can establish whether the implementation belongs in the project.
Verify the actual export path
Run the relevant project checks with safe fixtures and exercise the export through its real entry point. Cover authorized records, forbidden records, filter behavior, empty results, and special characters. Inspect the downloaded file rather than only checking that a request returned successfully. If the task fixes a bug, safely reproduce the original failure and confirm its absence after the change. Report any verification blockers instead of calling an untested result complete.
Use the same workflow for recurring maintenance
Once this process works for one feature, repeat it for fixes, integrations, documentation, and bounded refactors. Start free to evaluate Multos against your own repository. Consider a subscription when ongoing work needs additional capacity or paid model access. Measure reviewed changes and verified outcomes, not just generation speed. The useful result is software you can maintain and confidently extend again.
Ready to Get Started?
Bring an existing project to Multos and start with one bounded feature. Evaluate the reviewed diff and verified behavior free, then choose a plan around your recurring development work.
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