• 5 min read • Namanyay Goel

AI Keeps Changing Code It Shouldn't: The Drift Problem Explained

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Vibe coders call it drift: the bot edits files you never asked it to touch. Here’s why it happens and how to shut it down.

Drift is what happens when your assistant edits working code you never mentioned. Yesterday checkout was fine. Today a copy tweak "helpfully" rewrote the payment helper and everything fell over. If that sounds familiar, this guide is for you.

If you're experiencing this and feeling scared to make any more changes, you're not alone—we have a complete guide on overcoming that fear.

What Drift Is (in Plain English) #

Drift is incidental change. You request “update the onboarding headline,” and the AI:

  • Refactors utility functions to use a new helper
  • Renames components “for consistency”
  • Deletes a prop it deems unused

None of those actions roll back automatically. Multiply this by dozens of prompts per week, and your working code quietly diverges from what users rely on.

Why Drift Happens #

  1. Global reasoning: LLMs treat codebases holistically. When you mention a component, they scan related files and “improve” them by association.
  2. Ambiguous prompts: “Make this cleaner” invites broad refactors. Without explicit boundaries, the model defaults to system-level hygiene.
  3. Stale context: Your context window might contain older file versions. The model writes changes against stale state, causing merges that ripple across modules.
  4. Auto-formatting side effects: Even small diffs trigger project-wide formatting or import sorting, creating noise and hiding real changes.
  5. Lack of ownership signals: If nothing is marked “do not touch,” the model assumes everything is fair game.

Real Examples from the Field #

  • Background job drift: Asking Cursor to “retry smarter” rewrote the entire scheduler and disabled payment reminders.
  • Styling drift: A Tailwind tweak renamed components, breaking snapshot tests later.
  • API drift: Claude adjusted response types while tweaking copy, triggering TypeScript errors two folders away.

Each change seemed reasonable alone. Together, they wrecked user trust.

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How to Spot Drift Early #

1. Monitor Diff Radius #

Install a Git pre-push hook that counts files per branch. If a “copy tweak” touches 17 files, halt and review.

2. Track High-Churn Files #

Use git log --stat or tools like GitButler to identify files changed most frequently. If stable modules appear in the top 10, drift is happening.

3. Compare Test Failures to Prompts #

Keep a prompt log. When a regression surfaces, trace the prompts executed that day. If none explicitly touched the failing module, drift is the culprit.

4. Enable Observability Alerts #

Set Sentry alerts for modules labeled “do not drift.” If new errors appear after unrelated changes, investigate immediately.

Technical Explanation (Simple Version) #

Think of your repo as a graph: components import utils, routes call services, jobs enqueue tasks. When you ask the AI to edit a node, it sees linked nodes and “improves” them too. Without strict instructions, the model assumes transitive closure (everything connected is fair game). Guardrails break that assumption by marking boundaries.

How to Prevent Drift #

1. Rewrite Your Prompts with Boundaries #

Include a clause like:

Only modify files explicitly listed below. If you think another file must change, stop, explain why, and wait for approval.

Make this clause permanent in every workspace prompt.

2. Use File-Level Guardrails #

  • Lock directories via .cursorrules or tool-specific settings.
  • Configure repo permissions so certain paths require human review.
  • Maintain CODEOWNERS for critical modules.

3. Break Work into Micro-Prompts #

Instead of “Improve onboarding flow,” try:

  1. “Review onboarding form validation. Summarize issues.”
  2. “Generate a diff for validation improvements only.”
  3. “Add analytics event tracking. Do not touch validation.”

Micro-prompts focus the model and reduce collateral edits.

4. Pair Human Review with AI Drafts #

Require a human to scan every diff with a “consistency checklist”:

  • Files touched match the prompt?
  • Imports/shims changed unexpectedly?
  • Tests updated or deleted without reason?

5. Automate Drift Alarms #

  • GitHub Actions: Fail PRs if more than X files changed without label wide-change-approved.
  • Linters: Add ESLint/Tsup rules preventing certain imports.
  • Tests: Snapshot critical JSON schemas to catch silent type drift.

6. Version Your Prompts #

Every time you adjust the workspace prompt, commit it. If drift resurfaces, you can diff prompt versions to see what guardrail disappeared.

Tools That Help #

  • Cursory Rules / Windsurf guardrails: Restrict directories.
  • Giga context packs: Pre-baked prompt clauses that enforce “ask before touching.”
  • Prettier config locks: Prevent auto-formatting from reordering imports everywhere.
  • AI Change Reports: Ask the model to summarize which files changed and why; cross-check the diff.

Keep Drift on a Short Leash #

Summing up:

  • Write prompts that name allowed files.
  • Add approvals for “do not touch” folders.
  • Automate alarms so you spot wide diffs and schema changes fast.
  • Version your guardrails so you can roll back a bad instruction.

Drift is not destiny. It is a side effect of letting the bot improvise. Set boundaries, add signals, and keep a human in the loop.

Ready to lock down your repo? Grab Giga’s guardrail kit and ship without surprise edits.