Domain-Specific Data Generation Framework for RAG Adaptation

摘要

Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning capabilities of large language models (LLMs) with external retrieval to produce domain-grounded responses. Effectively adapting RAG systems to domain-specific settings requires specialized, context-rich training data beyond general-purpose question-answering datasets. Here, we propose RAGen, a scalable and modular data-centric framework for generating domain-grounded question–answer–context (QAC) triples tailored to diverse RAG adaptation strategies. These QAC triples serve as training signals for multiple RAG adaptation approaches; in this work, we demonstrate their use for contrastive fine-tuning of embedding models and supervised fine-tuning of LLMs under retrieved contexts. RAGen generates QAC triples by identifying key concepts within documents, producing diverse questions guided by Bloom’s Taxonomy–inspired principles, and pairing them with precise answers extracted from relevant contexts. Its modular pipeline incorporates semantic chunking, hierarchical concept extraction, multi-chunk retrieval, and curated distractor contexts to encourage robust reasoning. Designed for scalability, RAGen efficiently handles large and evolving document corpora without redundant processing, making it particularly suitable for dynamic domains like enterprise knowledge bases.