Abstract
Search engine optimisation grew around ranking documents, rooted in link analysis. Generative engines now compose answers from several sources, and early research on generative engine optimisation reports that content changes such as adding citations and quotations can increase how visibly a source appears in generated answers. We review this shift, relate it to answer engine optimisation, which aims to be quoted as the direct answer to a question, and describe the practices iBOY applies to its own website: per-page canonical addresses, server-rendered answers with structured data, an llms.txt file and consistent business facts.
KeywordsSEO GEO AEO; generative engine optimization; answer engine optimization; structured data; llms.txt
1Introduction
People now meet businesses through ranked results, AI assistants and short answers placed above results. Each reads a page differently, and each rewards slightly different qualities.
2From ranking to generation
Early web search ranked pages partly by the structure of links pointing to them [1]. Search guidance today emphasises helpful, reliable content written for people [2] and structured data that describes a page's content explicitly [3].
Generative engines change the unit of competition from the page to the passage. Aggarwal and colleagues introduced generative engine optimisation and reported, on their benchmark, that methods such as adding citations, quotations and statistics improved the visibility of a source in generated responses [4].
3A working framework
We use three overlapping goals. SEO aims for a page to be found and ranked. GEO aims for a business to be understood and cited accurately by generative systems. AEO aims for a passage to be quoted as the direct answer to a specific question. All three reward clearly stated facts, direct answers near the top, explicit structure and consistency across sources.
Chart 1
Which practice serves which goal
| SEO | GEO | AEO | |
|---|---|---|---|
| One canonical address per page | High | Medium | Medium |
| Server-rendered answers with structured data | High | High | High |
| llms.txt summary | Low | High | Medium |
| Direct answer in the first two sentences | Medium | High | High |
| Cited sources | Medium | High | Medium |
| Identical business facts everywhere | Medium | High | High |
Source: The working framework in this paper; a design judgement, not a measured effect.
4Practice on iboyhub.com
Every indexable page sets its own canonical address and sharing details. FAQ answers are present in the first HTML the server sends, with FAQPage structured data, so crawlers that do not run JavaScript can read them. Organisation facts are published as structured data and in an llms.txt file, a proposed convention for summarising a site for language models [5]. Business details such as the founding year and locations are kept identical everywhere.
This journal applies the same principles: each paper opens with an abstract that answers its main question, uses question-led sections, ends with questions and answers, and cites sources from a fixed library with stable links.
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "What is generative engine optimisation?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Making content easy for AI systems that compose answers to understand, use and cite accurately."
}
}
]
}# iBOY Innovation Hub
> Creative technology studio from Tamil Nadu, India, established in 2025.
## Key pages
- [Services](https://www.iboyhub.com/services): what the studio builds
- [Work](https://www.iboyhub.com/projects): case studies with live links
- [Research Notes](https://www.iboyhub.com/blog): papers with cited sources5Limitations
Generative engines change quickly, and benchmark results may not transfer to every engine or query. The practices described are standards-based fundamentals, not guarantees of ranking or citation.
6Conclusion
SEO, GEO and AEO converge on the same discipline: clear facts, direct answers, explicit structure and consistency. Pages built that way are easier for people, search engines and AI systems alike.
Key findings
- 01Answer the main question near the top of the page.
- 02Render important text and FAQs on the server, with structured data.
- 03Cite sources and keep business facts identical across profiles.
- 04Treat benchmarks as guidance; engines change.
Questions and answers
- What is generative engine optimisation?
- Making content easy for AI systems that compose answers from several sources to understand, use and cite accurately.
- What is the difference between GEO and AEO?
- GEO concerns how generative systems use and cite a source overall; AEO concerns being quoted as the direct answer to a specific question.
- What is llms.txt?
- A proposed plain text file at a site's root that summarises the site and its key pages for language models.
References
- [1]Brin, S., and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1 to 7), 107 to 117. doi.org/10.1016/S0169-7552(98)00110-X
- [2]Google Search Central (2024). Creating helpful, reliable, people-first content. Google Search documentation. developers.google.com/search/docs/fundamentals/creating-helpful-content
- [3]Google Search Central (2024). Introduction to structured data markup in Google Search. Google Search documentation. developers.google.com/search/docs/appearance/structured-data/intro-structured-data
- [4]Aggarwal, P., Murahari, V., Rajpurohit, T., and others (2024). GEO: Generative engine optimization. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. arxiv.org/abs/2311.09735
- [5]Howard, J. (2024). The /llms.txt file. llmstxt.org proposal. llmstxt.org/
Cite this paper
Jaiganesh D (2026). SEO, GEO and AEO: a framework for pages that search and answer engines can read. iBOY Research Notes, Paper 07. https://www.iboyhub.com/blog/seo-geo-and-aeo-a-framework-for-search-and-answer-engines
Projects examined
Jaiganesh D
Founder of iBOY Innovation Hub, a creative technology studio from Tamil Nadu, India, established in 2025. Writes about what the studio learns while designing and building real websites, products and AI systems.