Large language models (LLMs) have become common tools for answering questions about hardware description languages (HDL). However, their use introduces a critical problem: over-answering. According to a recent study based on 6,246 Stack Overflow posts with accepted answers, LLMs tend to provide correct content but wrapped in redundant alternatives (65.7%) and verbose padding (69.1%). Furthermore, nearly half of the answers (49.0%) do not fully align with expert solutions, although participants preferred LLM responses for readability (58.3%).
This over-answering is not merely a stylistic flaw: in hardware design, an imprecise or redundant answer can propagate into timing violations or non-synthesizable logic that only surfaces late in the design flow. Therefore, measuring and mitigating this behavior has become a priority. The study proposes a multi-agent framework that improves the quality of HDL answers generated by LLMs. It evaluates two structural metrics: the number of core answers (reflecting redundancy) and the length of non-core content (reflecting verbosity). With this framework, the core answer quality score increased from 3.71 to 4.67 (+0.96) and the non-core content quality from 3.72 to 4.23 (+0.51) on a five-point scale.
From a business perspective, these findings highlight the need to integrate artificial intelligence in a controlled manner within development environments. At Q2BSTUDIO, a company specialized in software development, we address this challenge by combining AI with sound engineering practices. We offer custom software solutions that incorporate AI agents trained for specific tasks, such as HDL code generation, but with built-in validation and conciseness mechanisms. Our AI services range from customized models to multi-agent frameworks that reduce over-answering, ensuring precise and efficient responses.
Moreover, over-answering is not limited to HDL. In any technical domain, when an LLM offers multiple alternatives without prioritization, users waste time and may choose a suboptimal solution. Companies need to implement cybersecurity systems to protect data during these interactions, as well as scalable cloud infrastructure (AWS/Azure) to deploy these agents securely. At Q2BSTUDIO, we also integrate Business Intelligence (Power BI) solutions to monitor the quality of generated responses, enabling real-time model adjustments.
The path to reliable AI in hardware design involves adopting clear metrics and modular architectures. The proposed multi-agent framework is an excellent starting point, but its effective implementation requires expertise in software development, cloud integration, and domain knowledge. At Q2BSTUDIO, we accompany companies throughout this process, from designing agent logic to deploying in production environments, always with a focus on quality and efficiency.
In summary, LLM over-answering in HDL is a measurable and mitigable problem. With the right tools—such as multi-agent frameworks, cloud infrastructure, and BI oversight—it is possible to obtain more concise answers aligned with expert knowledge. If your organization uses LLMs for technical tasks, having a technology partner like Q2BSTUDIO can make the difference between a redundant response and an optimal solution for your hardware.
