With the rise of Large Language Models (LLMs), researchers have become increasingly interested in their applications in EDA flows, particularly in specific subdomains such as serving as knowledge assistants and generating RTL code. In this study, we present a Retrieval-Augmented Generation (RAG) framework tailored to EDA task processing, named EDA-Adaptive RAG. This framework addresses the implicit semantics of EDA data and facilitates efficient knowledge acquisition through classification and enhanced retrieval, significantly enhancing LLMs ability to acquire EDA knowledge. Furthermore, we aim to integrate RAG into the design process as an EDA assistant application. Using RTL code generation as a case study, we demonstrate that the performance of RTL code generation can be enhanced through highly relevant retrievals provided by our RAG. The experimental analysis involves EDA Q&A tasks and RTL code generation evaluation. It is shown that our method outperforms the latest works in terms of both answer stability and code quality.