RAG Architecture & Retrieval-Augmented Generation
RAG Architecture improves AI accuracy by fetching relevant proprietary data and feeding it to the model before it answers.
RAG mitigates the Hallucination Tax by forcing the model to rely on verified, deterministic data rather than its own probabilistic memory, ensuring accuracy for enterprise applications.
Multi-Hop Causal Traversal Engine
Explore how concepts dynamically feed into each other across 1-hop, 2-hop, and 3-hop transitive relationships. Click any node to navigate the causal highway.
RAG Architecture & Retrieval-Augmented Generation
RAG Architecture improves AI accuracy by fetching relevant proprietary data and feeding it to the model before it answers.
Direct Relationships (2)
Transitive Neighbors (Connected via Hop 1)
Extended Causal Ripple Effects
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Frequently Asked Questions
Q:What is Retrieval-Augmented Generation (RAG)?
A technique that combines search (finding facts) with an LLM (generating text) to provide accurate answers based on your data.
Canonical Specification Origin
An architecture that grounds LLM outputs by retrieving relevant factual information from a proprietary database and injecting it into the prompt context before generation.
Corpus Interconnections
Richard Ewing artifacts developed around this canonical framework, including publications, execution tools, and diagnostic models.
External Adoption & Peer Citations
Documented instances where independent researchers, engineering teams, and publications have cited, implemented, or referenced this concept outside Richard Ewing’s ecosystem.
External Evidence: No independently verified references recorded yet.
This concept is part of Richard Ewing’s original baseline canon. External citations and implementations are added only upon rigorous empirical verification.
Inspectable Evidence Ledger
Classified evidence items supporting, extending, or refining this canonical research specification.
| Evidence Item | Publisher | Evidence Type | Strength | Role | Action |
|---|---|---|---|---|---|
| Governing Enterprise Data | CIO.com | Editorial | ★★★★★ | Origin | Inspect ↗ |
Recommended Citation
Ewing, R. (2026). "RAG Architecture & Retrieval-Augmented Generation." Richard Ewing Research Canon. Available at: https://www.richardewing.io/concepts/retrieval-augmented-generation
@article{ewing_retrieval_augmented_generation,
author = {Ewing, Richard},
title = {RAG Architecture & Retrieval-Augmented Generation},
journal = {Richard Ewing Research Canon},
year = {2026},
url = {https://www.richardewing.io/concepts/retrieval-augmented-generation}
}