ChaTCL: LLM-Based Multi-Agent RAG Framework for TCL Script Generation

Manually generating Tool Command Language (TCL) scripts is time-consuming and error-prone. Although large language models (LLMs) show promise in automating TCL script generation, they struggle with complex EDA tasks. They often fail to meet practical requirements and face compatibility issues across multiple tools. To address these limitations, we introduce ChaTCL, a multi-agent Retrieval-Augmented Generation (RAG) framework based on LLMs. Our contributions include: (1) creating fine-tuned datasets (NondomainT and DomainT) for TCL-specific LLMs, (2) designing a TCL-specific RAG framework with one-to- one retriever-database mapping to reduce hallucinations, and (3) implementing a multi-agent system that interprets user prompts and autonomously selects agents for script generation. Evaluated using TCLEval, ChaTCL outperforms models like ChatGPT-4.0- o and OpenAI o1-preview in generating accurate scripts across diverse tools and design stages.