AI RNG: Practical Systems That Ship
Most refactors fail for one simple reason: they ask reviewers to trust an intention. The diff is large, the behavior is implicit, and the risk is unclear. Even when the refactor is correct, it still feels dangerous, so it either gets blocked or it ships without real safety.
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A refactor rescue mission is the opposite. It is a controlled modernization where every step is reviewable, every risk has a guardrail, and the system stays stable while you improve it.
This approach works especially well with AI assistance, because AI can accelerate the mechanical rewrites, while you keep human control over contracts, tests, and rollout decisions.
Choose a target that is worth the effort
Not every messy module deserves a rescue mission today. Pick a module where modernization pays for itself.
Signals that a module is a good target:
- It causes repeated bugs or incidents
- It blocks new features with complexity and fear
- It is slow, costly, or fragile under load
- It has unclear boundaries and too many responsibilities
- It has high churn, which makes risk compound
Once you pick the target, define what success means in observable terms: a smaller surface area, fewer responsibilities, better tests, safer deploys.
Establish the safety net before you move anything
The rescue mission begins by creating proof of behavior.
A safety net can include:
- Characterization tests that capture current behavior at the boundary
- A small integration harness that exercises the critical flow
- A golden master output for a stable interface, used carefully
- Monitoring signals that confirm behavior after deployment
AI can help you propose test cases and edge cases, but the key is that tests attach to contracts, not internals. You want tests that remain meaningful even after the refactor.
Create a seam that makes change possible
Large modules often mix IO, business rules, and stateful orchestration. The fastest way to make them refactorable is to create a seam.
A seam is a boundary you can hold constant while you change internals.
Examples:
- Introduce an interface around a database layer
- Split parsing from effects
- Extract a pure function from a stateful pipeline
- Wrap external dependencies behind a small adapter
A good seam has two properties:
- It is easy to test.
- It reduces the blast radius of change.
AI is useful here to suggest seams by scanning code patterns, but you choose a seam that matches how the module is actually used.
Replace the insides in small, reviewable slices
Once a seam exists, you modernize by swapping internals behind it in small slices. The slices should be sized so that a reviewer can reason about them without heroics.
Practical slice patterns:
- Convert one function at a time into a pure helper with tests
- Replace one data structure at the seam and provide adapters
- Move one responsibility out to a dedicated component
- Remove one implicit global dependency and inject it
If you are using AI to perform rewrites, treat AI output as a draft diff. You still need:
- Tests that validate behavior
- A style and naming pass for clarity
- A performance check if the path is hot
- A security check if the module touches auth, input, or secrets
Keep the module deployable throughout the rescue
A rescue mission fails when it becomes a long-lived branch. You want the module to ship improvements continuously, because shipping forces discipline: the code must compile, tests must pass, and reality must agree.
This usually means adding incremental feature flags or toggles only when needed, and removing them when the new path is proven.
A helpful decision table keeps you honest:
| Change type | Best release strategy | What to verify |
|---|---|---|
| Pure refactor with no behavior change | Ship continuously with tests | Regression suite and metrics stable |
| Behavior change needed | Use feature flag and staged rollout | Contract tests, canary results |
| Performance-sensitive change | Gate behind config and benchmark | Latency and CPU profiles |
| High-risk migration | Dual-run with comparison checks | Divergence detection |
Remove the old path and simplify
The mission is not complete until the old complexity is gone. Leaving the old path around is how technical debt returns.
Your final steps should include:
- Delete dead code and unused utilities
- Remove temporary flags and compatibility adapters
- Collapse duplicate logic into one implementation
- Update docs and runbooks to match new behavior
This is also where many teams quietly skip work. Resist that. The system becomes simpler only when the old path is removed.
A rescue mission summary you can reuse
A safe modernization is a pattern, not a heroic event.
- Define a success outcome and boundary contract
- Build a safety net that tests the contract
- Create a seam that isolates change
- Modernize behind the seam in small slices
- Ship continuously with verification
- Delete the old path and simplify
If you follow this, the refactor becomes an engineering asset: it reduces future risk instead of creating a new source of fear.
Using AI without losing control
AI can accelerate a rescue mission, but only if you keep the right responsibilities human-owned.
AI is excellent at:
- Mechanical rewrites that preserve structure
- Suggesting smaller helper functions and clearer naming
- Converting repetitive patterns into consistent style
- Drafting tests once the contract is explicit
AI is not a substitute for:
- Defining the contract that must remain true
- Deciding whether performance regressions are acceptable
- Understanding domain-specific invariants and edge cases
- Choosing rollout and rollback strategy
A practical approach is to ask AI for small diffs: rewrite one function, extract one seam, generate tests for one contract. Then you review, run, and merge. The work stays incremental, and you avoid the trap of a giant AI-generated refactor that no one can safely audit.
Keep Exploring AI Systems for Engineering Outcomes
• AI Refactoring Plan: From Spaghetti Code to Modules
https://ai-rng.com/ai-refactoring-plan-from-spaghetti-code-to-modules/
• Refactoring Legacy Code with AI Without Breaking Behavior
https://ai-rng.com/refactoring-legacy-code-with-ai-without-breaking-behavior/
• AI Unit Test Generation That Survives Refactors
https://ai-rng.com/ai-unit-test-generation-that-survives-refactors/
• Integration Tests with AI: Choosing the Right Boundaries
https://ai-rng.com/integration-tests-with-ai-choosing-the-right-boundaries/
• AI Code Review Checklist for Risky Changes
https://ai-rng.com/ai-code-review-checklist-for-risky-changes/
Books by Drew Higgins
