Imagine getting instant, accurate answers to your trickiest EDA tool questions, straight from the official documentation. No more sifting through endless manuals or cryptic forums. That's the promise of Retrieval Augmented Generation (RAG), a powerful AI technique that's transforming how we interact with complex software. But off-the-shelf RAG models often stumble when faced with the specialized language and intricate workflows of Electronic Design Automation (EDA). This is where customized RAG steps in. Researchers have developed a new RAG framework specifically for EDA tool documentation, called RAG-EDA. It tackles the challenge head-on with three key innovations. First, it fine-tunes the "retriever" model using contrastive learning, teaching it to understand the nuances of EDA terminology and find the most relevant document chunks. Second, it uses a "reranker" distilled from a powerful proprietary LLM to filter out weakly related information, ensuring only the most pertinent details make it to the final stage. Finally, the "generator" model, an open-source chat LLM, gets a double dose of training—first on general EDA knowledge and then on specific question-answer pairs from EDA documentation. This allows it to generate accurate, comprehensive responses even to complex queries. To test their system, the researchers created ORD-QA, a benchmark dataset based on the OpenROAD documentation, containing 90 diverse questions. RAG-EDA outperformed other leading RAG models across the board, demonstrating its effectiveness in providing accurate and relevant information. The team also tested their model on a commercial EDA tool with equally impressive results, proving its adaptability to different platforms. The future of EDA documentation interaction is here, and it's intelligent, efficient, and readily available. By open-sourcing their benchmark and training dataset, the researchers are paving the way for even more advancements in AI-driven EDA tool assistance.