mlsanityData to 7 Oct 2026

AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systems

arXiv 2604.01395 ↗First posted 1 Apr 2026cs.SEcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Retrieval-augmented generation (RAG) systems are gaining traction in enterprise settings, yet stringent data protection regulations prevent many organizations from using cloud-based services, necessitating on-premises deployments. While existing blueprints and reference architectures focus on cloud deployments and lack enterprise-grade components, comprehensive on-premises implementation frameworks remain scarce. This paper aims to address this gap by presenting a comprehensive AI engineering blueprint for scalable on-premises enterprise RAG solutions. It is designed to address common challenges and streamline the integration of RAG into existing enterprise infrastructure. The blueprint provides: (1) an end-to-end reference architecture described using the 4+1 view model, (2) a reference application for on-premises deployment, and (3) best practices for tooling, development, and CI/CD pipelines, all publicly available on GitHub. Ongoing case studies and expert interviews with industry partners will assess its practical benefits.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 14 of its 28 references that are in the map. First posted on arXiv 1 Apr 2026.

Builds on

14 of 28 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation11 Aug
  2. 2025RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines3 Jun
  3. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices13 Jan
  4. 2024RAG Does Not Work for Enterprises31 May
  5. 2024Evaluation of Retrieval-Augmented Generation: A Survey13 May
  6. 2023Ragas: Automated Evaluation of Retrieval Augmented Generation26 Sep

Other references · 8

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  3. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 242
  4. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
  5. 2024Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Datacited by 52
  6. 2025MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoningcited by 24
  7. 2025Securing RAG: A Risk Assessment and Mitigation Frameworkcited by 3
  8. 2025Towards practicable Machine Learning development using AI Engineering Blueprintscited by 1

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Close in wording, no citation either way

  1. 2025Engineering the RAG Stack: A Comprehensive Review of the Architecture and Trust Frameworks for Retrieval-Augmented Generation Systems
  2. 2025An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
  3. 2023A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture
  4. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey
  5. 2024Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective
  6. 2026Overcoming the "Impracticality" of RAG: Proposing a Real-World Benchmark and Multi-Dimensional Diagnostic Framework
  7. 2026Securing the Agent: Vendor-Neutral, Multitenant Enterprise Retrieval and Tool Use
  8. 2025From Search to Reasoning: A Five-Level RAG Capability Framework for Enterprise Data