# Improving LLM-Powered EDA Assistants with RAFT

arXiv: https://arxiv.org/abs/2506.06500 · HTML: https://mlsanity.com/paper/2506.06500/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024)
- [ChipNeMo: Domain-Adapted LLMs for Chip Design](https://mlsanity.com/paper/2311.00176/) (2023)
- [ChatEDA: A Large Language Model Powered Autonomous Agent for EDA](https://mlsanity.com/paper/2308.10204/) (2023)
- [Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination](https://mlsanity.com/paper/2406.06575/) (2024)
- [EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD](https://mlsanity.com/paper/2405.06676/) (2024)

## Built on by (most cited first, 2 of 2)

- [Revolution or Hype? Seeking the Limits of Large Models in Hardware Design](https://mlsanity.com/paper/2509.04905/) (2025)
- [RF-Agent: A Practical Framework for Building Language Agents for RFIC Design](https://mlsanity.com/paper/2607.18772/) (2026)

## Abstract (arXiv)

Electronic design engineers often struggle to efficiently access relevant information for tasks like design verification and technology development. While large language models (LLMs) can enhance productivity as conversational agents, pre-trained open-source LLMs lack domain-specific knowledge for Electronic Design Automation (EDA). In a Retrieval-Augmented Generation (RAG) context, LLMs rely on external context but may still produce inaccurate responses. Retrieval-Augmented Fine-Tuning (RAFT) improves LLM performance, but acquiring labeled question/answer (Q/A) data in EDA is difficult. To address this, we propose using synthetic Q/A datasets to enhance LLMs with RAFT. Our results show that RAFT with synthetic data significantly boosts LLM performance for RAG-based EDA tasks. We also investigate the impact of using real user questions as Retrieval-Augmented Few-Shot (RAFS) examples for synthetic data generation. Additionally, we implement secure access control to ensure sensitive information is only accessible to authorized personnel. Finally, we assess the risk of data leakage and unintended memorization during fine-tuning with synthetic data, providing practical insights.