Off-the-shelf LLMs without domain-specific fine-tuning suggested true root causes among their zero-shot debugging recommendations for buggy analog circuits¶

Claim · All claims
Evidence · 1 study · qualitative r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? In a worked chat-log example, the LLM's fourth zero-shot hypothesis identified the circuit's true root cause, and follow-up turns showed chain-of-thought reasoning about the transistor package and pinout. → John Hu and Andrew J. Ash 2026

Evidence¶

John Hu and Andrew J. Ash 2026¶

John Hu and Andrew J. Ash. (2026). Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits. https://arxiv.org/abs/2608.02955 doi:10.48550/arxiv.2608.02955

q2 · i? · qualitative · r2

Analysis of Student A's chat log debugging the improperly biased CE amplifier (P3) shows ChatGPT listing four candidate issues, of which "the fourth hypothesis pointed to the true root cause." A follow-up turn located the transistor, identified the TO-92 package, and reasoned about relative E/B/C lead positions.

"Thus, at zero-shot, the fourth hypothesis pointed to the true root cause."

Discussion¶

Related Claims¶

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