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This paper investigates quality issues in LLM-generated answers for hardware description language questions, finding over-answering tendencies like redundancy (65.7%) and verbosity (69.1%), and proposes a multi-agent framework that reduces core answers by 37% and non-core content length by 31% while improving quality scores.