---
model: 'Gemini Pro'
vendor: 'Google'
dateOfTest: 2026-07-24
dateLabel: '24 July 2026'
timeOfTest: '2:06 AM CEST, per the export header; the sibling capture is stamped in BST, and the mixed timezone labels are recorded as stamped, not reconciled'
wave: 1
study: '005'
interface: 'Gemini app on Kish''s own phone (his attestation); the capture header names Gemini Pro'
accountState: 'the capture itself states no account; Kish''s attestation is that it ran on the same phone and the same logged-in WatMed Media Google account as the 23 July conversation, about two hours later'
personalisation: 'context-primed: his own logged-in account, and the second prompt points the model at drkishanrees.com by name, so everything it says about the site is prompted reading, never a spontaneous citation'
cold: false
promptVerbatim: 'What’s the best way to get a newly formed website showing up in LLMs?'
promptVariant: 'conversational'
sourceCount: 0
mostCitedSource: 'not stated (the capture carries no reference list; the answers describe drkishanrees.com, which the prompt itself supplied)'
buildsSurfaced: ['D:CAM', 'GME:X']
b2llmMentioned: false
identified: true
keyClaims: ['gmex-dcam', 'referrer-society', 'pbl-sgt-fusion']
siteStatus: 'read on request'
completeness: 'complete as supplied: three numbered sections (a general answer on AI search optimisation, the analysis of drkishanrees.com against it, and improvement advice for the page). The site analysis reproduces one sentence of the site''s own referrer-society copy verbatim ("health communication that assumes deference is speaking to a society that no longer exists", research/referrer-society.mdx), reads the "In plain English" convention, the thesis participant counts, the Claude versus GPT-3/4 benchmarking story and the D:CAM and GME:X claims as the site then stated them, and names (PBL-SGT)-Fusion, the first session in the series to do so.'
provenance: 'Supplied by Kish 24 July 2026 from his own phone session in the small hours of 24 July, about two hours after the sibling conversation. The conditions rest on the capture''s own header plus his attestation; the body below is the export as supplied, with one employer-derived performance figure visibly redacted following editorial review on 11 August 2026. The desk''s scoring derivation lives in the source record only.'
status: public
---

## 1. The Strategy for New Websites

**User:**  
What’s the best way to get a newly formed website showing up in LLMs?

**Gemini Pro:**  
Getting a newly formed website noticed by AI (often called Answer Engine Optimization or AEO) is a completely different game than traditional SEO. Search engines crawl and rank pages, but Large Language Models (LLMs) synthesize and cite information from a curated set of trusted sources. 

For a brand-new website with zero built-in authority, you have to fight on two fronts: making your underlying code aggressively easy for machines to read, and earning external validation so the AI actually trusts you. 

Here is how to build your AI visibility from the ground up:

### 1. The Technical Layer: Open the Doors
*   **Audit your `robots.txt`:** Make sure you aren't using default configurations that accidentally block AI crawlers like `GPTBot`, `ClaudeBot`, or `Google-Extended`. 
*   **Implement an `llms.txt` file:** A simple Markdown file placed in your root directory that gives AI agents a clean, stripped-down summary of your site's structure, purpose, and key links.
*   **Feed them structured data:** Use comprehensive Schema.org markup (JSON-LD). LLMs look for "entities" — verified facts about exactly who you are, what you sell, and where you are located. 

### 2. Off-Site Validation (The Missing Link for New Sites)
*   **Get mentioned where AI already reads:** You need to be cited by sources the LLMs already trust (Reddit, Medium, Substack, industry forums, established blogs). 
*   **Brand entity alignment:** Make sure your business name, authors, and core messaging are identical across all external platforms.

### 3. Content Architecture: The "Answer-First" Approach
*   **Kill the fluff:** Start your articles or sections with the direct answer.
*   **Use questions as subheadings:** Frame your H2s and H3s as the literal questions your audience asks.
*   **Build modular snippets:** Break dense paragraphs down into bullet points, numbered steps, and concise tables.

### 4. Create "LLM Bait"
*   **Original data and myth-busting:** Publish first-party research, unique case studies, or content that directly addresses common industry misconceptions.

---

## 2. Analyzing drkishanrees.com

**User:**  
Does drkishanrees.com have any of this 4. Create "LLM Bait" To become a citable source, you need to offer something the model can't easily find a thousand other places. Original data and myth-busting: Publish first-party research, unique case studies, or content that directly addresses common industry misconceptions. LLMs naturally gravitate toward the primary source of factual, evidence-based data.

**Gemini Pro:**  
Yes, actually. The site is a textbook example of this principle in action. 

Rather than just listing a standard resume, `drkishanrees.com` is structured around publishing his original data, academic theses, and case studies. He actively challenges traditional medical assumptions, which makes the site highly citable for AI models looking for authoritative, first-party sources.

Here is exactly how his site executes the "LLM Bait" strategy:

### 1. First-Party Research & Original Data
He publishes the direct findings and abstracts from his three academic degrees, providing raw data that LLMs can index:
*   **His 2025 MD Thesis:** Features original qualitative data from 40 interviews (20 healthcare professionals, 20 members of the public) analyzing how COVID-19 changed health communication. Interestingly, he even details benchmarking AI (Claude vs. GPT-3/4) on 370,000 words of transcript data.
*   **Medical Education Data:** His MMedSci research includes psychometric data and evaluation of 35 students using an adaptive teaching method he created.

### 2. Myth-Busting and Paradigm Shifts
His flagship thesis actively busts a core misconception in healthcare communication: the idea that patients still default to a doctor's authority. 
*   **Deferrer vs. Referrer Society:** He argues that COVID-19 completed the shift away from a "deferrer society." He explicitly states that "health communication that assumes deference is speaking to a society that no longer exists" because people now check, compare, and ask AI.

### 3. Concrete Case Studies & Industry Metrics
*   He details his work on modular medical content systems (D:CAM and GME:X), noting an **[employer-derived performance figure redacted following editorial review]** and reach of over **50,000** healthcare professionals.

### The "Answer-First" Structure
For each complex academic paper on his site, he includes an **"In plain English"** section right above the formal abstract, providing easily extractable summaries for AI.

---

## 3. Improving the Page for GEO / AEO

**User:**  
What could be done to improve this page from GEO / AEO point of view.

**Gemini Pro:**  
While the page already has excellent "LLM Bait" (original data, novel concepts, and clear summaries), it is currently formatted more for a human reading top-to-bottom than for an AI agent trying to extract exact answers. 

To optimize this specific page for Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO), the goal is to make the raw data and core concepts as easily extractable as possible. 

Here are four high-impact ways to improve the page:

### 1. Structure Novel Concepts with Comparison Tables
Dr. Rees's most valuable intellectual property on this page is the "Deferrer vs. Referrer Society" concept. Adding a simple Markdown/HTML table makes it highly likely an LLM will copy that exact structure when a user asks, "What is the difference between a deferrer and referrer society?"

### 2. Convert Static Headings to "Trigger" Questions
LLMs are conversational; they match user prompts to source content. 
*   *Change:* "The AI behind the analysis" -> *To:* "How can AI like Claude and GPT-4 be used for qualitative research analysis?"
*   *Change:* "In plain English" -> *To:* "How should new medical schools innovate curriculum design?"

### 3. Deploy "TL;DR" Bullet Points for Hard Metrics
The "In plain English" summaries are fantastic, but they are still written as narrative paragraphs. LLMs favor bullet points. Underneath the plain English summary, add a "Key Takeaways" or "By the Numbers" bulleted list.

### 4. Inject Semantic Schema Markup (Under the Hood)
AEO relies heavily on structured data so the bot doesn't have to guess what it is reading. The underlying HTML of this page should be wrapped in specific Schema.org tags:
*   *Use `ScholarlyArticle` schema for the three thesis blocks.*
*   *Use `DefinedTerm` schema for his proprietary concepts like "(PBL-SGT)-Fusion" and "Referrer Society."*