AI Strategies for Batch Cherry-Picking

AI Strategies for Batch Cherry-Picking | Generated by AI

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Strategies for Batch Cherry-Picking with AI

Yes, your logic is sound. When dealing with a large volume of commits (around 70), breaking them into smaller batches is a highly effective way to manage context window limits and ensure the AI remains accurate when resolving conflicts.

While you could do this in batches of 10, there is actually a more efficient “Hybrid” approach that uses Git’s native power for the bulk work and the AI for the “hard part” (conflicts).

Recommended Workflow: The “Stop-on-Conflict” Method

Instead of manually feeding 10 commits at a time, you can tell Git to do all 70 at once. Git will process them instantly until it hits a conflict. That is when you call in the AI.

1. Start the Batch Process in Terminal

Use the inclusive range syntax to start the cherry-pick:

# A is the commit BEFORE the start of your range
# B is the last commit you want to include
git cherry-pick A^..B

2. Let the AI Handle the Conflict

If Git stops because of a conflict:

Open your AI assistant (Claude Code, Cursor, or Copilot).
Prompt: “I am in the middle of a batch cherry-pick. Please resolve the conflicts in the current files, ensuring the logic from the cherry-picked commit is preserved while respecting the target branch’s existing code.”
The AI will fix the files.
You verify and run:
```
git add .
git cherry-pick --continue
```

3. Repeat

Git will automatically move to the next commit in the 70-commit sequence. If it applies cleanly, Git moves on. If there is another conflict, repeat Step 2.

Why this is better than “10 at a time”:

Efficiency: Git is faster at applying “clean” commits than an AI agent is at processing them through a chat interface.
Context Preservation: By only using the AI for conflicts, you save your context tokens for the actual code logic rather than wasting them on commits that Git could have handled automatically.
Linearity: It ensures you don’t accidentally skip or double-pick commits between your batches of 10.

Comparison of Methods

Feature	10-at-a-time (Manual Batch)	Stop-on-Conflict (Hybrid)
Speed	Slow (requires manual intervention every 10)	Fast (only stops when necessary)
Token Usage	High (AI reads all code for every commit)	Low (AI only reads conflicted files)
Risk of Error	Moderate (human error in range selection)	Low (Git manages the sequence)
Conflict Quality	Good (AI has full context of the 10)	Best (AI focuses strictly on the diff)

Important Tips for 70+ Commits:

Use a Temporary Branch: Always create a new “migration” branch before starting. Never cherry-pick 70 commits directly into your main production branch.
Verify Mid-way: Every 20 commits or so, run your test suite. If the AI makes a subtle logic error during a conflict resolution, it’s easier to catch it early than after all 70 are done.
Check for Dependencies: If commit #50 depends on a change in commit #5, but you skipped #5, the AI might hallucinate code to make it “work.” Ensure your range A^..B covers all necessary dependencies.

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