Retrieval-Augmented Generation Frameworks for Trustworthy Enterprise AI Systems

Abstract

Large Language Models (LLMs) have accelerated the adoption of artificial intelligence across enterprise applications, enabling natural language interfaces for tasks such as document search, customer support, software development, and business analytics. Despite these advances, standalone LLMs often generate inaccurate or unverifiable responses because their outputs rely primarily on knowledge acquired during pre-training. This limitation poses significant challenges in enterprise environments, where decisions must be based on current, organization-specific, and traceable information. Retrieval-Augmented Generation (RAG) addresses this challenge by combining semantic information retrieval with generative language models, allowing responses to be grounded in trusted enterprise knowledge sources. This article examines the architecture of enterprise RAG systems, covering document ingestion, embedding generation, vector indexing, retrieval strategies, prompt augmentation, and response synthesis. It also reviews mechanisms that improve trustworthiness, including source attribution, access control, governance, and human oversight. A comparative discussion of widely used RAG frameworks highlights their suitability for enterprise deployment, while current implementation challenges and emerging directions such as Graph RAG, Agentic RAG, and multimodal retrieval are also discussed. The study demonstrates that RAG has become a key architectural approach for building enterprise AI systems that are more accurate, transparent, and adaptable to continuously evolving organizational knowledge