  This paper presents RTLFixer, a novel framework enabling automatic syntax
errors fixing for Verilog code with Large Language Models (LLMs). Despite LLM's
promising capabilities, our analysis indicates that approximately 55% of errors
in LLM-generated Verilog are syntax-related, leading to compilation failures.
To tackle this issue, we introduce a novel debugging framework that employs
Retrieval-Augmented Generation (RAG) and ReAct prompting, enabling LLMs to act
as autonomous agents in interactively debugging the code with feedback. This
framework demonstrates exceptional proficiency in resolving syntax errors,
successfully correcting about 98.5% of compilation errors in our debugging
dataset, comprising 212 erroneous implementations derived from the VerilogEval
benchmark. Our method leads to 32.3% and 10.1% increase in pass@1 success rates
in the VerilogEval-Machine and VerilogEval-Human benchmarks, respectively.
