Reducing Hallucinations in Domain-Specific LLMs via LoRA Fine-Tuning: A Production Case Study

Large language models (LLMs) exhibit strong performance across natural 
language tasks, yet their tendency to hallucinate remains a fundamental 
barrier to deployment in domain-specific production settings. This paper 
presents a production case study demonstrating that LoRA fine-tuning on 
12,000 domain documents reduces hallucination rates by 22% relative to 
Llama 3.1 8B while cutting monthly inference costs by 58% compared to 
GPT-4. Combining LoRA with semantic paragraph-level RAG chunking yields 
a 31% improvement in retrieval precision measured by RAGAS faithfulness 
scores across 500+ evaluation queries.