  Retrieval augmented generation (RAG) enhances the accuracy and reliability of
generative AI models by sourcing factual information from external databases,
which is extensively employed in document-grounded question-answering (QA)
tasks. Off-the-shelf RAG flows are well pretrained on general-purpose
documents, yet they encounter significant challenges when being applied to
knowledge-intensive vertical domains, such as electronic design automation
(EDA). This paper addresses such issue by proposing a customized RAG framework
along with three domain-specific techniques for EDA tool documentation QA,
including a contrastive learning scheme for text embedding model fine-tuning, a
reranker distilled from proprietary LLM, and a generative LLM fine-tuned with
high-quality domain corpus. Furthermore, we have developed and released a
documentation QA evaluation benchmark, ORD-QA, for OpenROAD, an advanced
RTL-to-GDSII design platform. Experimental results demonstrate that our
proposed RAG flow and techniques have achieved superior performance on ORD-QA
as well as on a commercial tool, compared with state-of-the-arts. The ORD-QA
benchmark and the training dataset for our customized RAG flow are open-source
at https://github.com/lesliepy99/RAG-EDA.
