Jul 2026· International Journal of Information Technology Research Studies (IJITRS)· 0 citations· 4 references
TL;DR
This paper presents a technical synthesis of RAG for trustworthy enterprise assistants and describes the end-to-end pipeline in full: document chunking, embedding, vector indexing, retrieval, cross-encoder re-ranking, and grounded generation with inline citations.
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.
Large Language Models (LLMs) have emerged as powerful assets for recommender systems. However, deploying them as generative recommenders or zero-shot rankers at web-scale remains bottlenecked by prohibitive computational overhead and grounding challenges. In this paper, we revitalize the classic, highly efficient two-tower retrieval architecture by adapting LLMs as semantic representation backbones rather than generative engines. We introduce an LLM-native two-tower framework engineered for high-throughput, large-scale retrieval. Our architecture introduces several key innovations: a shared LLM encoder for joint user-item modeling, End-Of-Sentence (EOS) token pooling for compact sequence embedding, cross-dataset transfer learning, knowledge distillation from powerful cross-encoder teachers, and latent reasoning within the user tower. Extensive evaluation across three public benchmarks demonstrates that cross-encoder architecture outperforms current state-of-the-art (SoTA) models, while the efficient two-tower student achieves SoTA-comparable retrieval performance. Furthermore, experiments on internal large-scale production systems yield substantial topline retrieval improvements along with high resilience to model staleness and superior data scaling. Our findings demonstrate that when augmented with modern representation learning, the traditional two-tower paradigm remains an exceptionally competitive and practical solution for industrial retrieval systems.
Zhe Xu, Prachi Agrawal, Kavosh Asadi et al.· 0 citations
Due to the surge in digital document creation, intelligent systems that can effectively retrieve accurate information from unstructured PDF content have become a necessity. Current keyword-based search techniques lack semantic meaning, while large language models (LLMs), when utilized individually, produce hallucinated results when they are not contextually aware of document content. In this paper, we introduce a novel real-time question answering system for PDF content using a lightweight approach to Retrieval-Augmented Generation (RAG). Our system combines a HuggingFace sentence transformer model for semantic embeddings with a FAISS vector similarity search for efficient retrieval, leveraging a Groq-hosted LLaMA 3.3 70B model for context-grounded answer generation. The system is built using LangChain for orchestration and Streamlit for web-based deployment, and requires no GPU, no fine-tuning, and no pre-indexed corpus. Evaluation across five complementary metrics—Exact Match, Token-level F1, BLEU, ROUGE, and semantic similarity—demonstrates strong alignment between generated and reference answers, with a peak semantic similarity of 0.8709 and a mean Token F1 of 0.4701, confirming the viability of this approach for real-world document intelligence.
Mukesh Lakshmi Sai Medikonda, Kalva Vishnu Teja, K. Greeshma et al.· International Conference Com...· 0 citations
The Adaptive Multi-Stage Vector Retrieval (AMSVR) framework is proposed, prioritising weighted, drift-resistant composition over uniform fusion, and offers tailored configurations: AMSVR-Scientific (dense + tuned hybrid) peaks at NDCG@10 = 0.7570 on SciFact, while AMSVR-Full (seven stages) targets broader, noisier corpora where Recall@100 matters most.
Samsudeen Alabi Bankole, Yakub Kayode Saheed· NLP & Big Data· 0 citations
This work proposes RAGnRoll, a language model for attributed answer generation within a multi-round Retrieval-Augmented Generation (RAG) framework that leverages the iterative nature of multi-round RAG to train an LLM to incrementally build answers guided by subqueries.
Hanane Djeddal, Laure Soulier, K. Pinel-Sauvagnat et al.· ACM Transactions on Informat...· 0 citations