AI-Powered Knowledge Bases in Healthcare, Finance, and Technical Support
Author(s): Nan Wu
Publication #: 2507030
Date of Publication: 27.07.2025
Country: United States
Pages: 1-13
Published In: Volume 11 Issue 4 July-2025
DOI: https://doi.org/10.5281/zenodo.16501015
Abstract
This paper investigates the architecture and methodologies of modern AI-powered knowledge bases, emphasizing their role in delivering accurate, scalable, and context-aware responses across domains such as healthcare, finance, and technical support. We analyze core components—including knowledge sources, retrieval mechanisms, LLMs, and workflow orchestration—alongside associated techniques like embedding-based retrieval, prompt engineering, and human-in-the-loop processing. Using real-world systems and implementations as reference points, we present a comparative evaluation highlighting best practices for building reliable retrieval-augmented generation (RAG) pipelines tailored to domain-specific requirements and performance constraints..
Keywords: AI Knowledge Bases, Retrieval-Augmented Generation, Large Language Models, Healthcare Informatics, Vector Databases, Prompt Engineering, Human-in-the-Loop Systems.
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