Skip to main content
+1 332 241 7790

Whitepaper

Production RAG Architecture

Reference architecture for enterprise retrieval-augmented generation: hybrid retrieval, reranking, citation tracking, evaluation, and governance. With concrete trade-off analysis.

Published
February 10, 2026
Length
38 pages
AIRAGArchitectureWhitepaper

Abstract

RAG is the most common enterprise AI deployment pattern of 2026, and the most commonly mis-shipped. This whitepaper documents the reference architecture we use for production RAG across financial services, healthcare, and enterprise SaaS engagements: the components, the contracts between them, the trade-offs we evaluate at each layer, and the governance frame that makes the system audit-defensible.

The architecture below is platform-agnostic. We've shipped it on AWS Bedrock, Azure OpenAI, Vertex AI, and self-hosted vLLM stacks. The components change; the contracts don't.

Table of contents

  1. 01

    Why most RAG demos don't ship

  2. 02

    Reference architecture: components and contracts

  3. 03

    Hybrid retrieval: dense + sparse + filters

  4. 04

    Reranking: when, with what, evaluated how

  5. 05

    Citation tracking and refusal patterns

  6. 06

    Evaluation harnesses: faithfulness, groundedness, latency

  7. 07

    Cost optimization: model tiering, caching, context compression

  8. 08

    Governance: model cards, audit logs, vendor risk

  9. 09

    Deployment topologies: cloud, hybrid, self-hosted

  10. 10

    Appendix: vendor comparison matrix