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How to Safely Use AI Coding Assistants in 2026

AI coding tools are powerful but risky. Here's how to get the speed benefits without losing your code or introducing bugs.

AI coding assistants have changed how developers work. They can write boilerplate in seconds, refactor complex code, and help you explore solutions faster than ever before.

They can also delete your files, introduce subtle bugs, and confidently break things in ways that take hours to fix.

Here’s how to get the benefits while staying safe.

The Reality of AI Coding in 2026

Let’s be honest about where we are:

  • AI is faster than humans at many coding tasks
  • AI makes mistakes that humans wouldn’t make
  • AI doesn’t understand context the way you do
  • AI changes are hard to undo when they go wrong

The developers who thrive with AI aren’t the ones who never make mistakes. They’re the ones who can recover instantly when mistakes happen.

Rule 1: Set Up Protection First

Before you write a single prompt, make sure you can recover if things break.

The most reliable approach is automatic snapshots. mrq runs in the background and captures every file change:

npm install -g mrq-cli
mrq login
mrq watch

Now every change is captured, from any AI tool, any editor, any operation. When AI breaks something:

mrq restore abc123

Back to exactly where you were. No scrambling, no hoping you committed, no hunting through menus.

Alternative: Git commits before every AI operation. This works if you’re disciplined, but the one time you forget is usually the time AI goes haywire.

With protection in place, you can iterate as fast as AI allows. Without it, you’re gambling with your work.

Rule 2: Review Before Accepting

AI suggestions look convincing. They’re formatted well, they compile, and they often work. But “often” isn’t “always.”

For inline completions (Tab to accept):

  • Read the suggestion before hitting Tab
  • If it’s more than a few lines, read it carefully
  • Watch for hallucinated imports or function names

For multi-file changes (Composer, Copilot Edits, Claude):

  • Always use diff view
  • Check which files are being modified
  • Look for deletions, since AI loves to “clean up” code

Red flags to watch for:

  • Files being deleted or renamed
  • Imports from packages you don’t use
  • Changes to files you didn’t ask about
  • “Cleanup” or “refactoring” you didn’t request

This takes thirty seconds and catches most problems before they happen.

Rule 3: Be Specific About Scope

Vague prompts are the number one cause of unexpected AI behavior. When you say “clean up this codebase,” you’re giving AI permission to do almost anything.

Instead, be explicit about what you want and what you don’t want:

“Refactor the fetchUser function in api/users.ts to use async/await. Do not modify or delete any other files.”

The more constraints you provide, the more predictable the output.

Rule 4: Work in Small Increments

When you ask AI to build an entire feature at once, you get a massive diff that’s hard to review and impossible to partially revert.

Break work into small, reviewable chunks:

  • “Create the data model”
  • “Add the API endpoint”
  • “Build the form component”

Each step is easy to verify. If something goes wrong, you know exactly where.

With automatic snapshots running, you can restore to any increment, not just where you happened to commit.

Rule 5: Know What’s High Risk

Some operations are more dangerous than others:

High risk (have protection ready):

  • “Refactor the entire codebase”
  • “Clean up unused code”
  • “Reorganize the file structure”
  • “Update all dependencies”

Medium risk:

  • Multi-file changes
  • Touching configuration files
  • Modifying database schemas
  • Changes to auth/security code

Lower risk:

  • Single-file edits
  • Adding new code (not modifying)
  • Generating boilerplate
  • Writing tests

Adjust your caution level accordingly. For high-risk operations, make sure you can restore with one command.

Rule 6: Don’t Trust AI With Secrets

AI assistants can accidentally:

  • Hardcode API keys you mention in prompts
  • Add secrets to files that get committed
  • Expose credentials in error handling code

Best practices:

  • Never paste secrets into prompts
  • Use environment variables, not hardcoded values
  • Review AI changes for accidentally added credentials
  • Use secret scanning tools (mrq includes this)

Rule 7: Use Git for Milestones

Git remains the right tool for meaningful history. When you hit a milestone:

git add -A
git commit -m "User authentication complete"

This keeps your Git history clean and useful, with actual milestones instead of “checkpoint before AI” noise.

With automatic snapshots handling continuous protection, you don’t need to commit constantly. Use Git for what it’s good at: intentional, meaningful history.

The Safe AI Coding Workflow

Here’s what this looks like in practice:

# Start your session
mrq watch                    # Automatic protection starts

# Work with AI
# Iterate freely, accept suggestions, experiment
# Everything captured automatically

# Review changes
# Check diffs before accepting multi-file changes
# Watch for unexpected deletions

# Hit a milestone
git add -A
git commit -m "Feature complete"

# Something broke?
mrq restore abc123           # Back instantly

# End of day
# Git history is clean
# Every intermediate state is recoverable

The key: protection is always running. You can iterate as fast as AI allows because recovery is always one command away.

Summary

The developers who succeed with AI coding:

  1. Set up protection first — automatic snapshots, always running
  2. Review changes carefully — diffs before accepting
  3. Write specific prompts — scope and constraints explicit
  4. Work incrementally — small, verifiable chunks
  5. Use Git for milestones — meaningful history, not noise

AI makes you faster. Proper protection makes that speed safe.

mrq is continuous versioning for AI coding. Every change captured automatically, instant recovery when things break.

Written by mrq team