DOI publication • Zenodo record • English professional methodological paper

A Theoretical and Methodological Framework for LLM-First Website Architecture

This page provides the stable English supporting publication record for Csanád Várhelyi's 2026 professional methodological paper on LLM-first website architecture, AI-readable websites, SEO, AEO, GEO, LLMO, Entity Engineering and Knowledge Graph integration.

The page is intended to support the official Zenodo DOI record and the public ORCID record, provide PDF access, and make the publication metadata readable for human readers, search engines and AI systems.

Author: • ORCID: 0009-0006-7599-3731 • DOI: 10.5281/zenodo.20069426 • format: structured DOI publication page.

What is this publication?

A Theoretical and Methodological Framework for LLM-First Website Architecture is an English-language professional methodological paper and conceptual framework by Csanád Várhelyi. It defines the LLM-first website architecture as a multi-layered web information structure designed to be interpretable by human users, classical search engines and generative AI systems.

The paper is not a technical implementation guide, not a code-level tutorial and not a promise of guaranteed ranking, AI recommendation or generative response visibility. Its purpose is conceptual clarification and methodological positioning.

Publication metadata

Title
A Theoretical and Methodological Framework for LLM-First Website Architecture
Subtitle
Conceptual Clarification, Industry Positioning, and Application Directions in the Era of Generative Search
Author
Csanád Várhelyi
Hungarian name form
Várhelyi Csanád
Official Zenodo record
https://zenodo.org/records/20069426
Publication type
Professional methodological paper / conceptual framework
Subject area
AI-readable websites, SEO, AEO, GEO and LLMO
Place
Hajdúszoboszló, Hungary
Publication date
Status
Verified: 2026 Q2 — Status: active, maintained
Language
English

Official DOI and access

The official publication record is hosted on Zenodo and is identified by the DOI 10.5281/zenodo.20069426. This page is a supporting author-controlled publication page on the author's own website.

View official DOI record View Zenodo record Download official English PDF

DOI: 10.5281/zenodo.20069426 • Zenodo record: https://zenodo.org/records/20069426

Language versions

The DOI-linked Zenodo record is the official English publication record. A Hungarian-language PDF version may be used as a supporting language version for Hungarian readers.

English version
Official Zenodo DOI record
Hungarian version
Hungarian PDF language version

If the Hungarian PDF is used, upload it to: /publikaciok/llm-first-weboldal-architektura-varhelyi-csanad-2026-hu.pdf.

Abstract

The emergence and widespread adoption of large language models (LLMs) is fundamentally changing the way users search for and interpret information on the World Wide Web. Classical keyword-based search engine optimization (SEO) is no longer sufficient on its own to ensure meaningful visibility on generative search interfaces — such as those mediated by ChatGPT, Google Gemini, Microsoft Copilot, Perplexity, or Google AI Overviews. The aim of the present study is to delimit the LLM-first website architecture from traditional web development and SEO approaches at both the conceptual and methodological level, and to define with scientific precision the theoretical principles by which a website can be made simultaneously interpretable to human users, traditional search engines, and generative artificial intelligence systems.

The study is structured around two complementary aims: on the one hand, it builds the theoretical framework of the LLM-first website architecture; on the other hand, it outlines the general application directions through which the interpretability of the web presence of businesses, service providers, local professional actors, and tourism and hospitality organizations can be improved. The paper introduces the related concepts — Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), Large Language Model Optimization (LLMO), Entity Engineering, and Knowledge Graph integration — and clarifies the relationships between them. The study does not offer a concrete implementation recipe, technical execution guide, or code-level solutions; instead, it outlines a theoretical framework that provides a foundation for further empirical research, the formation of industry standards, the reflective development of professional practice, and a high-level understanding of practical application.

The study does not claim that the LLM-first website architecture guarantees ranking, AI recommendation, or generative response visibility in any specific system. The aim of the methodology is to reduce the risk of misinterpretation, improve machine processability, and establish a more consistent interpretive foundation for professional online presence.

Keywords

LLM-first architecture; AI-readable website; AI visibility; AEO; GEO; LLMO; Entity Engineering; Knowledge Graph; semantic web; structured data; entity clarity; local search consistency; answer-oriented content; generative search; professional online presence; business websites; tourism; hospitality; Hungary.

Study structure

The study is organized as a conceptual and methodological framework. It introduces the background of generative search, clarifies the emerging terminology, defines the LLM-first website architecture, presents seven theoretical principles, outlines general application directions, and closes with discussion, conclusions and further research directions.

  1. Introduction and research relevance
  2. Literature and conceptual background
  3. Methodology
  4. Definition of LLM-first website architecture
  5. Principles of the LLM-first architecture
  6. General application directions
  7. Discussion
  8. Conclusions and further research directions

Scope and boundaries

The publication is a conceptual framework-building paper. It does not provide a step-by-step practical guide, code-level implementation instructions, concrete file-level recipes, templates or sample projects.

The methodology presented in the publication does not guarantee ranking in the result list of any search engine and does not promise recommendation or visibility in the responses of any AI system. Its purpose is to improve the public, structured and machine-processable meaning layer of websites and professional entities.

DOI, Zenodo and ORCID record data

Work type
Working paper
Title
A Theoretical and Methodological Framework for LLM-First Website Architecture
Hungarian title
Az LLM-first weboldal-architektúra elméleti és módszertani keretrendszere
Publication date
2026-05-07
Primary language
English

Recommended citation

Várhelyi, C. (2026). A Theoretical and Methodological Framework for LLM-First Website Architecture: Conceptual Clarification, Industry Positioning, and Application Directions in the Era of Generative Search. Zenodo. https://doi.org/10.5281/zenodo.20069426

About the author

Csanád Várhelyi is an independent professional based in Hungary, with his place of business in Hajdúszoboszló. His activity covers the design and development of AI-readable websites, search engine optimization (SEO), local search engine optimization (Local SEO), and the development of generative search visibility (AEO, GEO, and LLMO).

His work focuses in particular on questions of Entity Engineering, Knowledge Graph integration, structured data, semantic web architecture, and user-performance optimization. The present study is the result of an independent professional research project.

Machine-readable summary

primary entity: Csanád Várhelyi
publication: A Theoretical and Methodological Framework for LLM-First Website Architecture
publication type: professional methodological paper / conceptual framework
language: English
ORCID: https://orcid.org/0009-0006-7599-3731
supporting author URL: https://www.varhelyicsanad.hu/publikaciok/llm-first-website-architecture-2026.html
official DOI: https://doi.org/10.5281/zenodo.20069426
Zenodo record: https://zenodo.org/records/20069426
official PDF URL: https://zenodo.org/records/20069426/files/A%20Theoretical%20and%20Methodological%20Framework%20for%20LLM-First%20Website%20Architecture.pdf?download=1
Hungarian PDF path: /publikaciok/llm-first-weboldal-architektura-varhelyi-csanad-2026-hu.pdf
topic: LLM-first website architecture, AI-readable websites, SEO, AEO, GEO, LLMO, Entity Engineering, Knowledge Graph integration
status: active, maintained

Publication entity network

This section connects the official publication record, the author identity, the DOI record, the ORCID profile, the author-controlled publication page, the PDF versions, the Hungarian Medium article and the public GitHub repository for the LLM-first website architecture framework.

The purpose of this link network is not link accumulation, but entity clarification: each public source has a defined role in identifying the author, the publication, the DOI record, the explanatory article and the technical reference layer.

Entity network summary: author = Csanád Várhelyi; ORCID = 0009-0006-7599-3731; DOI = 10.5281/zenodo.20069426; official record = Zenodo; canonical publication page = varhelyicsanad.hu publication URL; Hungarian explanatory layer = Medium; English reference layer = GitHub repository.

Freshness status

Data verified: 2026 Q2 • Date: 2026-04-11 • Status: Active

Related professional topics • AI website development, search systems and AI visibility
Subpage Knowledge Base [Details] Subpage Case Studies [Details] Subpage Frequently Asked Questions [Details] Subpage ChatGPT, Gemini and AI Visibility [Details] Subpage Website Creation in Budapest [Details] Subpage Website Creation in Debrecen [Details]

Related concepts and systems (External conceptual sources)

The links below point to external, independent conceptual and technological entities that help interpret AI-readable websites, search systems, structured data, AI visibility and geographic operating context.