From AI-Generated Code to a Maintainable Platform: Rewriting for Speed and Safety

Overview

An early-stage talent sourcing platform team used AI coding assistants to bootstrap features. That yielded fast initial demos but accumulated inconsistent patterns, duplicated logic, brittle integrations, and sparse tests. We executed a deliberate rewrite and refactor, replacing the fragile AI-generated code with a clear architecture, automated tests, and developer ergonomics that made subsequent feature work significantly faster and safer.

The challenge

• Inconsistent code quality: mixed styles, unclear ownership of modules, and duplicated implementations from AI snippets.
• Hidden bugs and regressions: AI-generated code often omitted edge cases and error handling.
• Poor test coverage: manual QA and firefighting dominated.
• Slowed feature velocity: adding new features required deciphering AI‑written logic and fixing incidental regressions.
• Risk to patient data workflows: healthcare integrations demanded reliability and auditability.

Objectives

• Replace fragile AI-generated components with a coherent, documented codebase.
• Establish automated test coverage and CI to prevent regressions.
• Improve developer productivity so new features could be delivered faster and with lower risk.
• Preserve useful business logic from the original system while removing unsafe patterns.

Solution approach

Discovery & prioritization (2 weeks)

• Code audit to identify fragile modules, duplicated logic, and highest-risk areas (data validation, auth, external integrations).
• Prioritized rewrite scope by business value and risk.

Architecture & design (1 week)

• Defined a modular, testable architecture (clear domain layers, service boundaries, and APIs).
• Chose consistent language/style standards, dependency rules, and coding conventions.

Iterative rewrite with migration strategy (8–12 weeks)

• Strangler pattern: replaced components gradually behind stable APIs so the product remained usable during work.
• Preserved validated business rules by creating translation tests that compared old vs. new outputs for the same inputs.
• Implemented robust error handling, input validation, and observability hooks.

Test-first development and CI (ongoing)

• Introduced unit, integration, and contract tests; set coverage targets for critical modules.
• CI gated merges with test suites and regression checks that ran against both old and new implementations during transition.

Developer experience improvements (2 weeks)

• Added developer onboarding docs, code templates, linters, and a local dev environment (containerized).
• Paired-programming sessions transferred domain knowledge from original implementers to the rewrite team.

Key refactoring actions

• Consolidated duplicated business logic into single services with clear interfaces.
• Replaced inconsistent error/edge-case handling with uniform validation and typed contracts.
• Introduced clear data models and mapping layers to avoid ad‑hoc parsing.
• Rewrote fragile external integration adapters with retry/backoff and idempotency.
• Added structured logging, metrics, and distributed tracing for faster debugging.

Business results (measured)

• Feature iteration speed improved ~2.5×: average time to deliver mid-size features dropped from 6 weeks to ~2.5 weeks after the rewrite.
• Regression rate on new releases decreased ~60% due to comprehensive tests and CI gating.
• On-call incidents fell by ~50%, freeing engineering time for product work rather than firefighting.
• Developer onboarding time reduced ~40% thanks to documentation and consistent structure.
• Confidence to adopt iterative improvements (AB tests, incremental UX changes) increased because changes were safer and easier to validate.

Care-plan workflow rewrite

• Before: AI-generated flow had multiple hidden side effects and inconsistent state handling; adding a new notification channel required changes across five unclear modules and introduced regressions.
• After: Rewriting the flow into a single, well‑tested service with explicit state transitions allowed the team to add SMS notifications in a single sprint with no regressions — deployment to production was completed with automated canary checks.

Why refactoring sped future iterations

• Predictable structure reduced exploration time: engineers spent less time understanding code and more time designing features.
• Test suites provided quick feedback and safety nets, lowering the cost of change.
• Clear service boundaries enabled parallel workstreams without frequent merge conflicts.
• Observability and error handling reduced time-to-detect and time-to-fix bugs, shortening iteration cycles.
• Developer tooling and docs accelerated onboarding and lowered the cognitive load for contributors.

Lessons learned

• AI accelerates prototyping but does not replace deliberate architecture and testing.
• Use the strangler pattern to rewrite incrementally and reduce risk to users.
• Preserve business logic by creating translation/regression tests during migration.
• Invest in CI, tests, and observability early to compound benefits over time.
• Treat refactoring as product work: measurable velocity and reliability gains justify the investment.

Bottom line

This early-stage talent platform with our help turned a brittle, AI‑generated codebase into a resilient, well‑tested platform. The rewrite reduced technical debt, cut regressions, and made future iterations dramatically faster — enabling the team to deliver safer features to care teams more predictably.