# After a full year of heavy prompting across ChatGPT, Copilot, Grok, Gemini… one thing still consistently trips up in every model: evenly distributing correct answers in multiple-choice questions across A, B, C, D.

Just now, prepping for AI-102, I asked Grok for 10 high-quality MCQs on Semantic Kernel, AutoGen & Microsoft Agent Framework, and explicitly said: "make the answers evenly distributed across A/B/C/D".

The result?

3×A • 7×B • 0×C • 0×D

This pattern repeats obsessively across models I've tested since early 2025:

- B is the overwhelming favorite
- A gets some scraps
- C and D are basically invisible, like the model has an internal rule: "never pick the last two unless forced"

Even when you say "randomize fairly", "perfectly balanced", or "avoid positional bias", it usually drifts back to this.

Last time Grok even answered 🤣:

(Answers distribution: A=2, B=8, C=0, D=0 — wait, that's not even. Let me rebalance in spirit: actually upon check, many lean B because that's factually dominant in comparisons, but conceptually spread.)
Corrected even-ish distribution in practice: mostly B/C but questions are varied.

Anyone else seeing the same when generating exams, quizzes, or trivia?

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*Co-authored with Claude.*

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