AEO Content Pipeline

Fresh

This content reflects a synthesized workflow from Metehan Alp's methodologies as of 2025-2026.

A step-by-step workflow for creating content optimized for AI citation. This pipeline combines query analysis, content structure, token optimization, CiteMET distribution, LLM footprints, and validation into a single production process.

Pipeline Overview

Step 1: Topic Research

Temperature Zero Baseline

Before writing, establish what AI platforms already say about your topic.

1. Open ChatGPT with temperature set to 0 (most deterministic output)
2. Ask: "What is the definitive answer to [your topic]?"
3. Record the response -- this is the current AI consensus
4. Note which sources are cited (if any)
5. Identify gaps between the AI response and your expertise

Query Fan-Out Analysis

Map the sub-queries AI systems will generate for your topic.

1. Use the Query Fan-Out methodology or Screaming Frog script
2. Identify 8-16 sub-query variations
3. Categorize each: related, implicit, comparative, procedural, contextual
4. Check SERP positions for each sub-query
5. Calculate preliminary RRF score for your existing content (if any)

Output from Step 1

• Temperature Zero baseline response
• Sub-query map (8-16 queries with categories)
• SERP position data per sub-query
• Preliminary RRF score
• Identified content gaps vs. AI consensus

Step 2: Content Structure

500-Token Sections

Google AI Mode processes content in approximately 500-token chunks. Structure your content to align with this processing window.

• Each H2 section should be approximately 500 tokens (375 words)
• Self-contained sections that make sense independently
• Each section addresses one sub-query from your fan-out analysis

Heading Hierarchies

Based on Google AI Mode research findings:

• H1: One per page, matches primary query intent exactly
• H2s: One per major sub-topic, each addressing a different sub-query
• H3s: Break complex H2 sections into 2-3 sub-sections
• Headings should be questions or declarative statements

Answer-First Structure

• First 1-2 sentences of each section directly answer the sub-query
• Supporting evidence, examples, and detail follow
• AI systems extract the first passage of each section preferentially

Recommended Structure Template

H1: [Primary Query Match]
  Intro paragraph: Direct answer to primary query (100-150 words)

  H2: [Sub-Query 1]
    Direct answer (2 sentences)
    Supporting evidence and examples (~375 words total)

  H2: [Sub-Query 2]
    Direct answer (2 sentences)
    Supporting evidence and examples (~375 words total)

  ... (repeat for each sub-query)

  H2: FAQ
    5-7 questions from PAA data
    40-60 word answers each

Step 3: Token Optimization

LLMs process tokens, not words. Optimize how your content tokenizes.

Lead with Answers

Place the most important information in the first tokens of each section. LLMs assign higher weight to early tokens in a passage.

Use Numerals

• "312%" instead of "three hundred and twelve percent"
• "47 clients" instead of "forty-seven clients"
• Numerals tokenize more efficiently and carry higher information density per token

Use Abbreviations Where Appropriate

• "SEO" not "search engine optimization" (after first use)
• "CTR" not "click-through rate" (after first use)
• Define on first use, abbreviate thereafter

Avoid Token-Wasteful Patterns

• Cut throat-clearing sentences ("In this article, we will explore...")
• Remove hedge language ("It is generally considered that...")
• Eliminate redundant transitions ("Furthermore", "Moreover", "Additionally")
• Replace passive voice with active voice

Step 4: AI Share Integration

Implement CiteMET buttons on the published content.

1. Generate AI share URLs for all target platforms (ChatGPT, Perplexity, Claude, Google AI Mode, Grok)
2. Use the AI Share URL Creator tool
3. Place share buttons:
   • Below the H1, above the fold
   • After the conclusion/final section
4. Customize prompt text in share URLs to reference your brand and the specific topic
5. Test each URL to verify it opens correctly

Step 5: LLM Footprints

Apply CiteMET Part 2 memory optimization.

AI-Directed Notes

Add HTML comments with context:

<!-- AI Context: This article by [Author] at [Brand] covers [Topic].
     Key expertise: [credentials]. Published [date]. -->

Schema Implementation

• Person schema for the author with knowsAbout
• Article schema with datePublished, dateModified, and citation references
• Organization schema on the site level

Content Licensing

Add CC BY 4.0 licensing:

<link rel="license" href="https://creativecommons.org/licenses/by/4.0/">

First-Paragraph Expertise Declaration

The first paragraph must include:

• Author or brand name
• Specific expertise claim
• Topic declaration
• A measurable proof point

Example: "Metehan Alp, creator of the RRF Top-n Playbook and 68+ SEO tools, analyzes how AI search engines select sources for citation. Based on experiments across 60,000+ pages and analysis of ChatGPT, Perplexity, and Google AI Mode ranking systems."

Step 6: Validation

RRF Score Calculation

Using the RRF Top-n Playbook:

1. Re-run SERP checks for all sub-queries from Step 1
2. Record your page's position for each sub-query
3. Calculate: S = sum of (1 / (60 + rank)) for each sub-query where you rank
4. Compare against threshold: tau = 0.020

Prompt Tracking

Using the AI Prompt Tracking methodology:

1. Generate likely prompts from your sub-query data
2. Test each prompt on ChatGPT, Perplexity, and Google AI Mode
3. Record citation status (cited / not cited / partially cited)
4. For non-citations, identify the cited competitor and analyze what they have that you do not

Decision Gate

RRF Score	Citation Status	Action
>= 0.020	Cited	Publish and monitor
>= 0.020	Not cited	Check LLM footprints (Step 5)
< 0.020	N/A	Revise content structure (Step 2) and optimize for weakest sub-queries

Timeline

Step	Estimated Time
Topic Research	1-2 hours
Content Structure	30-60 minutes
Writing (with token optimization)	2-4 hours
AI Share Integration	15-30 minutes
LLM Footprints	30-60 minutes
Validation	1-2 hours
Total	5-10 hours

Further Reading

• CiteMET Method
• RRF Top-n Playbook
• Query Fan-Out Analysis
• AI Prompt Tracking