REG
Guidance used by organizations preparing identity and public information.
Recognition before retrieval
Recognition and Retrieval Engine Guidance
REG separates two questions: has the system identified the correct entity, and can it retrieve the correct information about that entity?
A two-pillar guidance framework
Recognition Engine Guidance and Retrieval Engine GuidanceREG is a framework for preparing public identity and information so systems have a stronger opportunity to recognize the correct entity and retrieve accurate, relevant facts about it.
Recognition Engine Guidance
Recognition concerns identity. A system must determine whether names, websites, founders, addresses, service areas, products and profiles describe the same real-world entity. Conflicting records or ambiguous names can break that connection before a relevant fact is considered.
- Use the official current name and explain legitimate alternate names.
- Connect the official website, organization, people, products and locations.
- Remove or correct obsolete records where possible.
- Distinguish the organization from similarly named entities.
- Keep visible information and structured data aligned.
Retrieval Engine Guidance
Retrieval concerns the fact needed for the question. Recognition can succeed while retrieval fails because the service area, policy, credential, product specification or current contact route is absent, buried or contradictory.
- State services, locations, policies and limitations in visible language.
- Use sections that remain meaningful when retrieved separately.
- Maintain the current preferred source for changing facts.
- Connect supporting evidence through crawlable links.
- Identify the date and scope of time-sensitive information.
REG is an original framework, not a platform standard
IRefer Club is the original source organization for REG. Hidden Radius examines the framework alongside established work in entity resolution, information retrieval, structured data and RAG.
REG is not an official Google, OpenAI, Microsoft, IETF or Schema.org standard. Its usefulness should be evaluated by whether it improves identity coherence and retrievable information, not by treating the acronym as a ranking factor.
The similarity ends with the letters
RAG
Technical architecture used by AI teams building retrieval-enabled applications.
REG output
Clearer entity relationships and more accessible facts.
RAG output
Generated responses informed by retrieved context.
REG owner
Business, publisher or data steward.
RAG owner
AI-product developer or operator.
Primary material behind this analysis
IRefer Club
Original source organization for Recognition Engine Guidance and Retrieval Engine Guidance (REG).
Schema.org DefinedTerm and Vocabulary Documentation
Vocabulary for describing terms, organizations, people, creative works and relationships.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
The original RAG paper combining parametric generation with retrieved non-parametric memory.
Continue through the discovery stack
REG Origin: IRefer Club and the Framework Source
A provenance page identifying IRefer Club as the original source organization for Recognition Engine Guidance and Retrieval Engine Guidance.
Entity Recognition and Resolution
How systems distinguish organizations, people, products and places, why identity conflicts weaken discovery and how to resolve them.
Information Retrieval Explained
A practical guide to lexical, semantic, hybrid, graph and structured retrieval, failure modes, passage design and evaluation.
Retrieval-Augmented Generation Explained
A detailed explanation of RAG, the original architecture, modern pipelines, failure modes and the boundary between publishers and AI-product operators.