PIKE-RAG

In recent years, Retrieval Augmented Generation (RAG) systems have made significant progress in extending the capabilities of Large Language Models (LLM) through external retrieval. However, these systems still face challenges in meeting the complex and diverse needs of real-world industrial applications. Relying solely on direct retrieval is insufficient for extracting deep domain-specific knowledge from professional corpora and performing logical reasoning. To address this issue, we propose the PIKE-RAG (sPecIalized KnowledgE and Rationale Augmented Generation) method, which focuses on extracting, understanding, and applying domain-specific knowledge while building coherent reasoning logic to gradually guide LLMs toward accurate responses.