LLMs are becoming a discovery layer. Users ask a question, the model synthesizes an answer, and then it may cite a few sources. That shifts the goal from “rank and win a click” to “be the most useful, extractable, verifiable source in the retrieval set.”

For TalentHacked.com (UK Global Talent Visa platform), this is a solvable engineering problem: ship content that a headless retriever can fetch, chunk, embed, and cite.

The key concept: Information Gain

Information Gain is the practical reason an LLM would cite your page instead of any other page on the topic. It is the gap between:

A simple rule: if your page reads like every other overview, it has low information gain. If it contains atomic answers, clear definitions, and step-by-step checks that can be validated, it has high information gain.

Examples for TalentHacked:

Now let’s implement four steps that increase information gain and retrieval success.

Step 1) Publish llms.txt in the root

Purpose: give AI crawlers and retrieval agents a canonical map of the pages that are designed to be cited. Keep it small, opinionated, and stable.

Create https://talenthacked.com/llms.txt:

# llms.txt for TalentHacked.com
site: https://talenthacked.com
name: TalentHacked
description: Evidence-driven guidance and tools for the UK Global Talent Visa.
language: en-GB
last_updated: 2026-03-05

citable:
  - /uk-global-talent-visa
  - /evidence/letters-of-recommendation
  - /evidence/checklist
  - /glossary
  - /sources

disallow:
  - /search
  - /tag/
  - /author/
  - /*?*

Implementation notes:

Why this helps: retrieval systems often need a “starting set.” llms.txt reduces crawl ambiguity and points the model to your highest-signal pages first.

Step 2) Add semantic entity mapping with JSON-LD (TechArticle + DefinedTerm)

Purpose: make your content machine-resolvable. GEO is not just about keywords; it is about entities and definitions that can be referenced consistently.

Put JSON-LD in the <head> of a canonical guide page. This compact graph does three things:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://talenthacked.com/#org",
      "name": "TalentHacked",
      "url": "https://talenthacked.com",
      "sameAs": [
        "https://www.gov.uk/global-talent"
      ]
    },
    {
      "@type": "TechArticle",
      "@id": "https://talenthacked.com/uk-global-talent-visa#article",
      "headline": "UK Global Talent Visa: Evidence Guide (RAG-Friendly)",
      "publisher": { "@id": "https://talenthacked.com/#org" },
      "mainEntityOfPage": "https://talenthacked.com/uk-global-talent-visa",
      "about": [
        { "@id": "https://talenthacked.com/glossary#global-talent-visa" },
        { "@id": "https://talenthacked.com/glossary#endorsement" }
      ],
      "isBasedOn": [
        {
          "@type": "WebPage",
          "name": "UK Global Talent visa guidance (primary source)",
          "url": "https://www.gov.uk/global-talent"
        },
        {
          "@type": "WebPage",
          "name": "Home Office",
          "url": "https://www.gov.uk/government/organisations/home-office"
        }
      ]
    },
    {
      "@type": "DefinedTerm",
      "@id": "https://talenthacked.com/glossary#global-talent-visa",
      "name": "Global Talent visa",
      "description": "A UK visa route for leaders or potential leaders in eligible fields; typically requires endorsement or qualifying award.",
      "inDefinedTermSet": "https://talenthacked.com/glossary",
      "sameAs": "https://www.gov.uk/global-talent"
    },
    {
      "@type": "DefinedTerm",
      "@id": "https://talenthacked.com/glossary#endorsement",
      "name": "Endorsement",
      "description": "A decision by an endorsing body supporting eligibility for the Global Talent route.",
      "inDefinedTermSet": "https://talenthacked.com/glossary"
    }
  ]
}
</script>

Why this helps: when definitions are stable and linked, retrievers can target “definition chunks,” and the model can cite your site for terminology instead of a random blog.

Step 3) Write for vector retrieval: the “Atomic Answer” chunk pattern

Most RAG systems chunk by headings and paragraph boundaries. Your job is to make the first chunk after a heading:

Pattern:

Example:

<h2>What counts as “evidence” for a Global Talent application?</h2>
<p><strong>Atomic answer:</strong> Evidence is a set of verifiable documents that support specific criteria claims such as impact, recognition, and leadership. Each item should be attributable, dated, and easy to audit. Strong evidence maps one claim to one artifact and includes a one-sentence explanation of what it proves and why it is credible.</p>
<p>Details: add issuer, URL, screenshots, a criterion mapping table, and common failure modes...</p>

Operational tips:

Step 4) Verify SSR and crawlability with the headless test

If your site renders content only after client-side JS, many crawlers will see a thin shell. Test what an AI-style fetcher gets.

Fetch HTML as an AI bot:

curl -A "GPTBot" -L https://talenthacked.com/uk-global-talent-visa | head -n 80

You should see:

Quick checks:

curl -A "GPTBot" -L https://talenthacked.com/uk-global-talent-visa | grep -n "<h2"
curl -A "GPTBot" -L https://talenthacked.com/uk-global-talent-visa | grep -n "application/ld+json"

Common failure modes:

The fix is always the same: ensure SSR delivers meaningful HTML.

Technical Checklist

The Machine-Readable Web wins citations

The future web is not only human-readable. It is machine-retrievable. If you want LLMs to cite you, engineer your site for Information Gain: publish unique, auditable answers, define your entities, and make content work without JavaScript.