Improve GEO (Generative Engine Optimization): llms.txt, structured data, E-E-A-T signals
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@spencercjh ya está trabajando en esto.
Desde el 2/11/2026.
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Descripción
Summary
The site currently has little optimization for GEO (Generative Engine Optimization) — the practices that help AI answer engines (ChatGPT, Perplexity, Google AI Overviews, Claude, etc.) discover, understand, and cite our content. An AITDK scan of https://project-hami.io/docs/core-concepts/architecture surfaced a cluster of gaps; I verified each one against the repo and most are confirmed.
This is increasingly important for an infrastructure project: developers now ask AI assistants "how does HAMi work?" / "HAMi vs time-slicing" and we want HAMi to be the cited, authoritative source.
✅ What we already do well (foundation is strong)
static/robots.txtis already AI-forward: explicitAllow:rules forGPTBot,OAI-SearchBot,Claude-Web,anthropic-ai,Google-Extended,CCBot,Applebot-Extended,Amazonbot,Bytespider, plusContent-Signaldirectives and a sitemap reference. This is ahead of most projects — keep it.
So this issue is not about blocking/unblocking crawlers. It is about the layer above: giving those crawlers structured, citable, trustworthy content.
Findings
1. Machine readability — no llms.txt
- No
llms.txtorllms-full.txtinstatic/. The llms.txt spec is a concise, LLM-friendly index of a project's most important pages. Low adoption today, but it's a near-zero-cost differentiator. - Fix: add
static/llms.txt(curated summary + links to architecture, quickstart, GPU virtualization, FAQ) and optionallystatic/llms-full.txt. Docusaurus servesstatic/*at site root automatically.
2. Structured data — no JSON-LD site-wide
docusaurus.config.jsheadTagsonly inject the locale-redirect script, gtag, and favicons. Noapplication/ld+jsonanywhere except a narrow use insrc/components/EventLanding.js.- Missing:
Organizationschema (anchors "HAMi" as an entity in knowledge graphs)WebSiteschema withSearchAction(enables sitelinks search box)- Per-doc
TechArticle/ArticlewithdateModified(we already compute last-updated time —showLastUpdateTime: true— but don't emit it as schema)
- Fix: add
Organization+WebSiteJSON-LD viaheadTags; add doc-levelTechArticleschema via a small theme swizzle (theme-classic/DocItemhead) or a postBuild plugin that walks the docs metadata.
3. Author & date provenance — no byline, no author schema
- Docs plugin sets
showLastUpdateAuthor: false(docusaurus.config.js:217). No author attribution on docs, and noauthor/datePublished/dateModifiedin any schema. - Anonymous content is cited less by AI engines (provenance/trust signal).
- Fix: either re-enable
showLastUpdateAuthoror add explicitauthorsfrontmatter on key docs, and surface them in theTechArticleschema above.
4. Content depth — thin architecture.md stub
- The scanned URL
/docs/core-concepts/architecturemaps todocs/core-concepts/architecture.md, which is a ~170-word stub that just lists 4 components and links to an image. - Meanwhile
docs/core-concepts/hami-architecture.md(~18 KB) is the real, detailed architecture doc. We have two overlapping architecture pages and the thin one is the one AI engines are likely to surface/quote. - Fix: either (a) expand
architecture.mdinto the citable 300–3000-word range, or (b) merge it intohami-architecture.mdand redirect the route, so the authoritative version is what gets indexed. Option (b) is cleaner.
5. Trust / E-E-A-T — missing About, Privacy, Terms pages
src/pages/hasindex,community,events,case-studiesonly. No dedicated About, Privacy Policy, or Terms pages.- The footer copyright links to
https://lfprojects.org/policies/(covers trademark/terms) but there is no in-site Privacy or Terms link, and no About page in nav or footer. Their absence is a basic trust red flag for both users and AI citation. - Fix: add
/about,/privacy,/termspages (or at minimum surface the lfprojects policy + a short privacy statement) and link them in the footer alongside the existing Documentation/Community/Follow Us columns.
6. Content citability — question-style headings + outbound citations
- Docs rarely use question-style headings ("How does HAMi isolate GPU memory?", "What is the difference between HAMi and time-slicing?"). Sections phrased as user questions get cited more in AI answers.
- Few outbound citations to authoritative sources (upstream K8s device-plugin docs, NVIDIA MIG, etc.).
- Fix: where natural, phrase section headings as questions and add a short "References" list with outbound links on conceptual docs.
Suggested priority
| Priority | Item | Effort |
|---|---|---|
| P0 (quick wins) | llms.txt; expand/merge thin architecture.md |
S |
| P1 | Organization + WebSite JSON-LD; footer About/Privacy/Terms |
M |
| P2 | Per-doc TechArticle schema w/ dateModified; author bylines |
M |
| P3 | Question-style headings + outbound citations across conceptual docs | L (ongoing) |
References
- llms.txt spec: https://llmstxt.org/
- Schema.org
TechArticle/Organization: https://schema.org/TechArticle - Google structured data docs: https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data
- Scan source: AITDK report on
https://project-hami.io/docs/core-concepts/architecture
Happy to split this into separate issues or open a PR for the P0 items if helpful.
- Lenguaje dominante
- JavaScript
- Estrellas
- 11
- Forks
- 111
- Merge medio
- 2 d 1 h
- PR fusionados (30 d)
- 70
Preparar el entorno
- Sin Dockerfile ni archivo de Docker Compose
- Tiene una plantilla de pull request
- Leer la guía de contribución
Primeros pasos
- Lee el issue completo y luego la guía de contribución del proyecto.
- Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
- Haz un fork del repositorio y trabaja en una rama.
- Abre un pull request que haga referencia al número del issue.
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