Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination
Luyao Shi, Michael Kazda, Bradley Sears, Nick Shropshire, Ruchir Puri

TL;DR
Ask-EDA is an AI-powered chat agent for electronic design engineers that combines large language models, hybrid retrieval techniques, and abbreviation de-hallucination to provide accurate, relevant, and expert-level guidance across various design tasks.
Contribution
This paper introduces Ask-EDA, a novel design assistant that integrates LLM, hybrid RAG, and ADH techniques to improve response accuracy and relevance in electronic design engineering.
Findings
Hybrid RAG improves recall by over 40% and 60% on key datasets.
ADH enhances abbreviation resolution recall by over 70%.
Ask-EDA effectively responds to design-related inquiries.
Abstract
Electronic design engineers are challenged to find relevant information efficiently for a myriad of tasks within design construction, verification and technology development. Large language models (LLM) have the potential to help improve productivity by serving as conversational agents that effectively function as subject-matter experts. In this paper we demonstrate Ask-EDA, a chat agent designed to serve as a 24x7 expert available to provide guidance to design engineers. Ask-EDA leverages LLM, hybrid retrieval augmented generation (RAG) and abbreviation de-hallucination (ADH) techniques to deliver more relevant and accurate responses. We curated three evaluation datasets, namely q2a-100, cmds-100 and abbr-100. Each dataset is tailored to assess a distinct aspect: general design question answering, design command handling and abbreviation resolution. We demonstrated that hybrid RAG…
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Taxonomy
TopicsDigital Rights Management and Security
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Attention Dropout · Linear Warmup With Linear Decay · Weight Decay · WordPiece · Residual Connection · Softmax · Layer Normalization · BERT
