Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for grounding large language models in domain-specific knowledge. However, the relative effectiveness of different retrieval strategies for highly technical Electronic Design Automation (EDA) documentation remains understudied. We present a systematic comparative evaluation of four retrieval methods—Simple Vector Search, MultiVector Retrieval, RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval), and ColBERT (Contextualized Late Interaction over BERT)—across three EDA documentation corpora of varying complexity: online API documentation (324 chunks), a static timing analysis textbook (292 chunks), and a dense EDA tool command reference manual (648 chunks). Using an LLM-as-Judge evaluation framework with GPT-4o-mini scoring retrieved chunks on a 0–3 relevance scale across four query categories, we find that no single method dominates all scenarios. MultiVector Retrieval excels on structured online documentation (+40% over baseline), while RAPTOR and ColBERT achieve the highest relevance (2.0/3.0) on complex textbook content. For dense command references, both MultiVector and RAPTOR tie (1.6/3.0) while ColBERT’s token-level matching shows diminished returns. We provide cost-performance analysis showing ColBERT achieves competitive quality at zero API cost, and discuss implications for production EDA RAG system design. All benchmark code and the open-source dataset are publicly available.
Gursimran S Sodhi (Wed,) studied this question.