A Hybrid RAG-LLaMA Framework for Scalable and Accurate Interpretation of Legal Texts

Interpreting Indian tax law is challenging due to frequent legislative amendments, complex statutory structures, and the requirement for precise citation. This paper presents TaxFlow, a domain-specific legal assistant that integrates a hybrid Retrieval-Augmented Generation (RAG) framework with the LLaMA-3 language model for statutory question answering. The system is trained on a curated corpus of Indian tax statutes, government gazettes, and case law, processed through structured extraction, segmentation, and dense–sparse indexing. TaxFlow incorporates temporal validity filtering to ensure that retrieved provisions reflect the legally effective version at query time. The architecture combines FAISS-based retrieval, legal-domain adapters, and critique-driven generation to reduce hallucinations and improve citation fidelity. Evaluation is conducted on a unified Indian tax law benchmark using automatic metrics. Experimental results show that TaxFlow achieves 96.5% accuracy and a 95.0% F1-score, demonstrating consistent and substantial improvements over representative baseline systems, while BLEU and ROUGE-L scores demonstrate significant improvements in linguistic quality. The findings confirm the effectiveness of domain-adapted hybrid RAG systems for reliable and scalable legal assistance.