Retrieval-Augmented Generation for Trustworthy Enterprise LLM Assistants

Abstract

Large language models (LLMs) have changed enterprise knowledge work. Their value, however, is capped by three failures: they hallucinate, their parametric memory is frozen and grows stale, and they cannot read the proprietary data that holds most business answers. Retrieval-Augmented Generation (RAG) targets all three. It grounds generation in passages fetched at inference time from an external, continuously updatable corpus, so answers become verifiable and citation-backed without any model retraining. This paper presents a technical synthesis of RAG for trustworthy enterprise assistants. The end-to-end pipeline is described in full: document chunking, embedding, vector indexing, retrieval, cross-encoder re-ranking, and grounded generation with inline citations. Advanced variants are then surveyed, namely hybrid sparse-dense retrieval, Hypothetical Document Embeddings (HyDE), graph-based RAG, and agentic iterative retrieval. A RAGAS-style evaluation method quantifies faithfulness, answer relevance, and context precision and recall. On an illustrative enterprise question-answering scenario, an advanced configuration that combines hybrid retrieval with cross-encoder re-ranking lifts faithfulness from 0.71 to 0.91 and context precision from 0.62 to 0.84 over naive dense-only RAG. Agentic retrieval reaches 0.95 faithfulness, but pays for it in latency. Enterprise concerns, including document-level access control, data security, cost, and latency budgets, are treated as first-class design constraints. The reported metrics are illustrative. They characterise representative trade-offs rather than a specific deployed study.