LLM SEO Explained: How to Get Your Content Cited in AI Tools

Traditional SEO was built for ranking pages. But today’s LLM-driven search is built for retrieving passages.

In tools like Google AI Mode, ChatGPT, Gemini, and Perplexity, users no longer search with simple keywords. These LLMs, or Large Language Models, are using natural language prompts filled with context, constraints, and intent. The systems answering them analyze your content at the passage level, looking for dense, context-matched, self-contained answers.

What this means:

Pages don’t rank since only chosen passages are retrieved
Keyword coverage isn’t enough—entities, intent, and structure now matter more
You don’t compete for one query; you actually compete across a hidden matrix of synthetic queries
Formatting is no longer cosmetic since semantic chunking drives retrievability

In simple words, LLMs are now extracting and remixing different pieces of content that fit the full logic of a user’s prompt. If your content doesn’t align with the system’s reasoning, it doesn’t show up, no matter how high you rank.

This guide will walk you through how to fix that. We will cover:

How LLMs interpret prompts using entities and fan-out logic
How to structure content for modular, standalone retrievability
How to optimize for scenario-based prompts instead of keywords
How to engineer content to be cited, not just crawled

Let’s dive right in!

Example #1: LLM SEO Strategy for the Face Painting Ideas Page

Let’s take an example of a fictional client who runs a face painting school. They have a landing page dedicated to face painting ideas, but their page has a cluttered layout. They have too many images per face painting idea, and they also have too many tutorials.

Why is this a problem for such a visual service?

Well, this can easily overwhelm readers, especially beginners, with visual clutter, dilute topical focus, and make it difficult to scan or act on the content. This is called a cognitive load, and it can reduce the clarity of the presented content, and then this results in lower engagement, meaning that people are not going to interact with the content shown, and they can click away.

First Step: UX Optimization Recommendations

Right off the bat, we would suggest that the client:

Reduces the number of multimedia files per idea
Limits each section to 1 featured image + 1 core tutorial

This makes each idea easier to scan, less cognitively overwhelming, and allows the content to breathe.

Now let’s move on to the LLM optimization!

Second Step: LLM Optimization

So, how can we use LLM SEO principles for clarity, retrievability, and contextual alignment? We now know that real users are no longer searching by using simple keyword phrases like: “best face painting ideas.”

Instead, users are now entering complex, scenario-rich prompts into Gemini, ChatGPT, or Perplexity, such as:

“What are the best face painting ideas for a Disney-themed kids’ birthday party with characters like Elsa, Buzz Lightyear, and Moana?”

“Creative face painting ideas for school carnival booths that are quick to apply and feature animals like tigers, butterflies, and unicorns.”

“Show me the most popular face painting designs for Halloween 2025, especially for characters like Wednesday Addams, Spider-Man, and Taylor Swift’s Eras Tour look.”

“Face painting inspiration for music festivals like Coachella or Burning Man—ideas with neon, glitter, tribal patterns, and cosmic themes.”

You can see that each of these prompts contains multiple entities and context signals:

Event type (birthday party, carnival, music festival)
Target audience (young kids, teens, adults)
Skill level (quick design, beginner-friendly)
Character or theme (Elsa, Tiger, Coachella-style)

To match these prompts, each painting idea on the client’s page needs to:

Be framed as a standalone, context-aware passage
Include the relevant entities (event, age, design, difficulty, theme)
Use formatting that’s LLM-friendly: bullet points, bold sub-labels, and clear visual separation.

Before vs After: From Traditional SEO to LLM SEO

How does this look in action? Now we will show you an example of legacy SEO copy and how we can easily format it.

“Face painting is one of the most fun activities for kids and adults alike. Unicorns are always a huge hit when it comes to popular face painting ideas, and there are so many ways to do unicorn face painting! You can paint a unicorn on the cheek, forehead, or even do a full face if you want something extra special. Glitter and rainbows make everything better, and it’s all about creativity. You don’t need to be an expert either—just follow these simple steps and you’ll wow everyone at any event. Unicorns are magical, colorful, and loved by everyone!”

What are the issues with this approach?

❌ Written for broad readability, not retrieval
❌ Focused on surface descriptions, not user scenarios
❌ No entities or structured context (e.g., age group, event type, skill level)
❌ Lacks discrete, standalone passages that LLMs can extract
❌ Uses filler language and vague adjectives (“super cute,” “WOW your clients”)
❌ Overwhelming with visuals and lacks semantic clarity

And now let’s look at the LLM-optimized content:

“Unicorn face paint is a magical and beginner-friendly design for girls, perfect for birthday parties, school fairs, and fantasy-themed festivals. Artists can either paint a full unicorn mask or a smaller character design above the eye using pastel colors, sparkles, and one-stroke techniques.

👧 Age group: Girls aged 3–9
🎉 Occasions: Birthday parties, school fairs, and festivals
🎯 Skill level: Beginner
🧰 Techniques: One-stroke rainbow, white linework, optional glitter
🎨 Color palette: Pastel rainbow with white and gold accents
🧑‍🎨 Tools: Round brush, pastel split cake, skin-safe glitter gel
🦄 Characters: Classic unicorn, baby unicorn, winged unicorn (pegacorn), sleepy unicorn

Tutorial: Easy Unicorn Eye Design

Load pastel colors and sweep a rainbow shape above the eye.

Paint a simple unicorn head and horn in white using a round brush.

Add stars or chunky glitter to complete the effect.

👉 Watch the full video tutorial →”

Why does this approach work?
This structure allows LLMs to:

✅ Include retrievable entity combinations (e.g., “unicorn + pastel + beginner + one-stroke”)
✅ Match to parent or artist-led prompts that describe context, not just keywords
✅ Pull and cite this passage independently, without the rest of the page
✅ Target specific user contexts (e.g., “face paint for birthday parties for 3–9 y/o girls”)

Simply put, every idea is in one standalone, optimized module. And see how that was so much easier to read and scan?

Let’s take a closer look at the differences between traditional and LLM-optimized SEO copy:

Aspect: Purpose

Traditional SEO Copy: Target known keyword intent and drive organic visibility

LLM-Optimized Copy: Match real user prompts and LLM retrieval logic

Aspect: Writing Style

Traditional SEO Copy: Clear and accessible, often guided by keyword presence and readability

LLM-Optimized Copy: Specific, contextual, structured

Aspect: Content Structure

Traditional SEO Copy: Logical flow of information, often paragraph-based

LLM-Optimized Copy: Modular, clearly labeled sections for each idea

Aspect: Audience Relevance

Traditional SEO Copy: Broad appeal across segments and intents

LLM-Optimized Copy: Scenario-specific (e.g., “ages 3–9, birthday parties, beginner-friendly”)

Aspect: Entity Usage

Traditional SEO Copy: Entities included when relevant, sometimes generalized

LLM-Optimized Copy: Explicit (e.g., unicorn, pastel colors, one-stroke technique, glitter)

Aspect: Retrievability by LLMs

Traditional SEO Copy: Structured for human readability and crawlability, less optimized for passage-level chunking

LLM-Optimized Copy: High (self-contained, chunkable, prompt-aligned)

Aspect: Prompt Fit

Traditional SEO Copy: Designed for searcher queries and SERP features, not always aligned with natural language prompts

LLM-Optimized Copy: Optimized to match scenario-rich prompts with entities and context

Quick Summary

So, what have we learned so far?
Each painting idea on the page should be optimized as:

A modular answer block to prompt-style queries
A modular answer block containing event type, audience, skill level, theme, design specifics, and media

This ensures:

Better UX for scanning and focus
Better semantic match with LLMs
Higher retrievability for voice search, AI Overviews, and assistant-style browsing

Now that we’ve covered that, let’s move on to a more complex example.

Example #2: LLM SEO for the Best Organizational Chart Software

In traditional search, users would often simplify queries, like “best organizational chart software.” But, as we already mentioned, in today’s LLM-powered search interfaces (Gemini, ChatGPT, Perplexity), queries look more like:

“What’s the best org chart software for a healthcare organization with 1,000 employees, mostly remote teams, and HIPAA compliance?” or:

“What’s a good org chart builder that works on Mac, syncs with Slack and Google Workspace, and costs under $50/month?”

Does this mean that LLMs don’t rank pages? Correct! They are extracting passages that match an entire contextual need. Which means that they have additional context from the user, such as:

Industry context (e.g., healthcare, law firms)
Size of organization (e.g., 20-person startup vs 2,000+ enterprise)
Functional roles (e.g., HR, IT)
Technological stack (e.g., Slack, Mac, Google Workspace)
Constraints and criteria (e.g., HIPAA, SOC 2, <$50/month)

So, unless you write specific, retrievable passages that match these complex combinations of user attributes, your content will not be cited by LLMs, even if your page ranks #1 on Google!

How to fix this?

First Step: Understand the Topic and Its Entity Space

This step is about moving beyond the keyword to fully understand the semantic landscape of the topic. You’re not just targeting “best organizational chart software” – you’re mapping the entities that shape user needs, LLM comprehension, and retrievability.

Understanding the entity space of your topic is critical not just for content coverage, but for understanding what user context will look like when people search using LLMs. Every prompt a user types is composed of one or more entities (e.g., industry, tool, compliance requirement), and your goal is to know what those entities are before you even start writing.

When someone asks Gemini or ChatGPT: “What’s the best org chart software for a 1,000-person healthcare company using Slack with HIPAA needs?”, this isn’t just a long query; it’s a multi-layered prompt filled with contextual cues and entities that the LLM will try to understand and reason through.

Here’s what the LLM breaks down internally from the prompt above:

Organization size (1,000 employees): implies enterprise-grade tools.
Industry (healthcare): suggests the need for sector-specific solutions.
Tech stack (Slack): signals that collaboration tools must be compatible.
Compliance requirement (HIPAA): filters for platforms with verifiable regulatory safeguards.

If your content doesn’t contain a passage that speaks to all, or at least most of these elements, it will likely be skipped!

To highlight, LLMs look for passages that mirror the structure of the prompt, not just the keyword. You need to write for scenarios, not just terms.

What Even Are Entities?

Let’s take a step back. What are entities, you might be wondering?

Entities are the named people, places, products, categories, and standards that LLMs recognize and use to build semantic relationships, such as:

Brands (Lucidchart, Pingboard, Creately)
Features (real-time collaboration, drag-and-drop interface, template libraries)
Standards (HIPAA, SOC 2, GDPR)
Roles (HR, IT manager, compliance officer)
Integrations (Slack, Google Workspace, PowerPoint, HRIS)

LLMs use entity co-occurrence and embedding similarity to understand what your content is about and whether it’s relevant to a user’s prompt, meaning if you don’t include the right entities, your content won’t show up, because the model doesn’t see it as relevant. If your content doesn’t answer the question or scenario implied by the prompt, it won’t be selected either. LLMs are looking for passages that not only contain the right terms but deliver the complete, context-specific answer a user would expect.

Understanding your entity space also means understanding how users describe problems. You can use LLMs not only to list entities, but also to help structure those entities into an ontology, a conceptual model of how tools, features, roles, and needs relate to one another.

E.g., Advanced Prompt to ChatGPT:
“Act as a technical content strategist. Build a structured ontology for the topic ‘organizational chart software.’ Include classes such as product features, user types, industry applications, integrations, pricing tiers, and compliance needs. Output the relationships and hierarchy in a table or bullet format that could be used for content modeling.”

To illustrate how to structure entities meaningfully, here’s a sample ontology – a conceptual map of how relevant concepts relate to one another in the domain of organizational chart software:

Relationship: has_feature

Entity & Entity Instance: Product Features > Real-time collaboration, drag-and-drop editor, templates, PDF export, auto-sync

Relationship: used_by

Entity & Entity Instance: User Types > HR managers, IT admins, team leads, executives, and compliance officers

Relationship: used_in_industry

Entity & Entity Instance: Industry Use Cases > Healthcare, education, tech startups, government, non-profits

Relationship: integrates_with

Entity & Entity Instance: Integrations > Slack, Microsoft Teams, Google Workspace, HRIS, SharePoint

Relationship: offers_pricing_tier

Entity & Entity Instance: Pricing Tiers > Free, under $10/month, mid-tier ($10–50), enterprise pricing

Relationship: supports_compliance_with

Entity & Entity Instance: Compliance Needs > HIPAA, SOC 2, ISO 27001, GDPR

So, now that you know your entities, what should you do with them? You want your content to reflect these entities naturally in paragraphs, headings, tables, comparisons, and even schema markup. That way, your passages are aligned with how LLMs represent and retrieve knowledge, increasing your visibility, and how users naturally structure their prompts. You’re not just writing content; you’re creating answers that reflect the way people describe their needs to AI.

Second Step: Query Fan-Out

This step uses LLMs or specialized tools to generate the kinds of natural-language queries that real users ask, representing follow-ups, clarifications, and constraints. It mimics the way users interact with LLMs in real time.

LLMs don’t just process one query; they simulate user conversations. People start with a broad question, then clarify, add constraints, or ask for comparisons, such as:

“Best org chart software for a healthcare org?”
“Does it support HIPAA?”
“What about Slack integration?”
“Can I use it on a Mac?”

Now, how should you generate fan-out prompts?

Prompt this to ChatGPT/Gemini: “Act as an SEO strategist for a SaaS company. Based on the topic ‘best organizational chart software,’ generate 25 diverse, high-context prompts a user might input into an AI assistant like ChatGPT or Gemini during the search and decision process. Cover different industries, compliance needs, integrations, budget levels, features, and company sizes.”

Tools: Qforia, AlsoAsked

Here are also some sample fan-out prompts:

“Best organizational chart software for a 2,000-person healthcare enterprise needing HIPAA compliance and Slack integration”

“Free org chart builder for nonprofits with remote staff and Mac compatibility”

“Lucidchart vs OrgChart Now for medium-sized legal firms”

“Best org chart software for startups scaling past 100 employees”

“Org chart tools under $25/month that integrate with HRIS systems”

“Visual org chart builder for schools using Google Workspace”

“Best drag-and-drop org chart creator with PowerPoint export”

“What org chart tools let me automate updates from our employee directory?”

“Affordable alternative to Visio that runs in-browser and supports SOC 2 compliance”

“Which org chart platforms work best for hybrid teams and include collaboration features?”

This fan-out set will guide you on what you need to answer, which entities must be included, and how specific your content needs to be. The goal here is to simulate every major intent path a user could take around your core topic.

Third Step: Build a Query Matrix

This step is about organizing the prompts you generated in Step 2 into a structured view of user intent. You’re no longer guessing what topics to cover; you’re now categorizing prompts into clear themes that represent the real-world needs of your users.

Think of this as creating a coverage map of questions that you need to answer. You’re grouping queries by shared intent, not by keyword volume or search difficulty. This is not technical, it’s intuitive: what kinds of things are people asking about?

Here’s what you need to do:

Review your fan-out prompts (Step 2).
Group them into buckets based on intent themes, such as:
- Industry
- Budget
- Features
- Compliance
- Comparison
- Platform
For each group, pick example prompts that capture the full context a user might describe.
Use this matrix to plan which types of passages you need to write.

Here are some examples:

Intent Theme & Example Prompt
Industry > “Best org chart software for law firms with 50-100 staff”
Budget > “Free org chart builder for NGOs”
Compliance > “HIPAA-compliant org chart for hospitals”
Features > “Org chart tool with Slack + HRIS integration”
Comparison > “Lucidchart vs Pingboard for remote teams”
Platform > “Org chart tool that works offline on Mac”

By organizing your prompts by intent, you will be able to:

Identify all the different situations users need answers for
Ensure you’re not missing an entire use case (like budget buyers or Mac users)
Create a repeatable structure for scaling LLM-ready content

We build this matrix because it’s not realistic to write content for every single possible prompt variation. Instead, by grouping prompts by recurring intent patterns, we can focus on the most common and meaningful use cases, and write 2–3 high-quality, context-rich passages per group. This balances completeness with practicality and ensures the page addresses real user needs in a scalable way.

Let’s look at the comparison table once again:

Traditional SEO vs LLM SEO

Build keyword clusters vs Build prompt + entity-based matrices

Focus on page-level structure vs Focus on passage-level, context-matched coverage

Write general content by topic vs Write precise content that matches real-world user context

This structure becomes your semantic coverage plan. LLMs aren’t looking for one answer; they’re matching subtopics. If you fail to address the core scenarios, your brand won’t be represented in that part of the conversation.

Fourth Step: Structure Content Using the AI Mode Framework

You are probably wondering how you should structure your content, your landing page copy, and your blog posts. Let’s look at the core content traits that you can use for AI mode. Also, remember that these are not optional. They are signals that LLMs rely on to figure out what to retrieve, rephrase, or cite when answering complex prompts.

Trait: Reasoning Target

Description: Each passage must answer a full prompt

Why It Matters: LLMs use vector + logic to validate self-contained relevance

Trait: Fan-Out Compatible

Description: Include recognized brands, tools, and features

Why It Matters: Ensures matchability to the semantic concepts embedded in prompts

Trait: Citation-Worthy

Description: Include numeric values, features, pros/cons, and statistics

Why It Matters: Increases trust and likelihood of being cited

Trait: Composition-Friendly

Description: Use bullets, tables, TL; DRs, and headings

Why It Matters: Makes passages easily chunked by LLMs

(Learn more about how AI mode works.)

When a user types a complex prompt, LLMs don’t just scan it for matching phrases; they reason through sub-decisions that are implied within the prompt.

Example Prompt: “Best org chart tool for a healthcare organization with HIPAA compliance and Slack integration”

The LLM interprets this by silently asking itself:

What tools are considered high-quality org chart platforms?
Which ones are HIPAA-compliant?
Which ones work well for healthcare orgs?
Do they integrate with Slack?

Each of these is a reasoning step. Your content needs to match them, not just in general, but explicitly and clearly within retrievable passages. You’re not just matching a prompt, you’re matching its implied logic path.

To satisfy this path effectively in your content, use these formatting patterns:

Mini-FAQs: Each Q&A pair should answer a single real-world prompt fully.
Tables: Comparison tables (e.g., one software vs the other similar software) are highly chunkable and citation-friendly.
Bulleted Feature Lists: Clear formatting makes semantic parsing easier.
Use-case callouts: “Best for HR teams in remote settings” adds context that LLMs can match.
TL;DR summaries: Include a 1–2 sentence takeaway for each section.
Stat Blocks: Isolated statistics with sources improve citation likelihood.
Pros and Cons Boxes: Highlight tradeoffs clearly for reasoning engines.
Step-by-Step Instructions: Numbered processes enhance task-oriented retrievability.
Quote Blocks: Authoritative expert insights increase trust and passage-level value.
Persona-Based Segments: Structure responses by persona types (e.g., “For IT admins…”).
Semantic Headings: Use question-based or intent-labeled H2s/H3s (e.g., “Which tool is HIPAA-compliant?”).
Inline Comparisons: “X vs Y” phrasing within body paragraphs supports comparative fan-out.

Think of your content as a modular knowledge base, not a linear article. Each module should be optimized to stand on its own, include high-value entities, and be surfaced in response to a specific context-rich prompt.

Fifth Step: Write Optimized Passages

We now know that each passage is a retrieval-ready block; a self-contained unit of content that directly answers a full-context AI prompt. The LLM is not retrieving your whole page; it’s just looking for the single most semantically aligned passage that fits a user’s situation.

Writing optimized passages means anticipating the exact combinations of:

User needs (e.g., a nonprofit on a tight budget)
Entities (e.g., specific tools, pricing, platforms)
Functional outcomes (e.g., Slack integration, PDF export, compliance)

These passages become the actual surface that gets retrieved, cited, or paraphrased by LLMs.

Let’s take a look at this example for a user prompt:
“What’s the best org chart tool for a hospital that needs HIPAA compliance and remote team access?”

And here’s an example of an optimized passage on a fictional website:
“Z and Y are popular org chart platforms for healthcare organizations. Both offer HIPAA-compliant architecture, including encrypted data storage, role-based access control, and audit trails. These tools support remote access while maintaining regulatory compliance, making them suitable for multi-site hospitals.”

Why is this an optimized passage?

Entity instances used: name of org chart platforms, HIPAA, encrypted data, access control, audit trails, healthcare organizations, remote access, multi-site hospitals
Context matched:
- Industry: healthcare
- Constraint: HIPAA compliance
- Tech requirement: remote access
- Scale: multi-site

Let’s take a look at another user prompt:
“Is there a low-cost org chart software for nonprofits with fewer than 50 people that integrates with Google Workspace?”

And here’s also another example of an optimized passage:
“Y starts at just $5/month and includes drag-and-drop templates, real-time collaboration, PDF export, and integration with Google Workspace. It’s well-suited for nonprofits and budget-conscious teams under 50 people.”

Why is this an optimized passage?

Entity instances used: name of the software, $5/month, drag-and-drop, real-time collaboration, PDF export, Google Workspace, nonprofits, teams under 50
Context matched:
- Audience: nonprofit
- Budget: <$10/month
- Tech stack: Google Workspace
- Scale: small teams

Now what would be a good passage if a user is interested in comparing two software products? For example, a user would type this prompt:
“Z vs Y for distributed teams with HRIS integration”

Optimized passage could look something like this:
“Z offers powerful diagramming tools with HRIS integrations and team collaboration features, but Y stands out for remote teams with real-time org chart updates, Slack sync, and employee profile customization. For distributed HR teams, Y may offer more relevant remote-first features.”

This works because you have:

Entity instances used: actual names of the products, HRIS, Slack, remote teams, employee profiles
Context matched:
- Comparison format
- Remote/distributed team needs
- Integration: HRIS, Slack

Depending on your product, you can always highlight if it’s compatible for Mac users, whether it offers offline access, if it integrates with other industry standard software such as Microsoft Teams, Slack and others.

Why Does This All Matters in LLM SEO?

LLMs operate on semantic similarity between the user’s prompt and your content passage. They’re looking for answers that demonstrate:

Precision (does it match the full context?)
Structure (can it stand alone?)
Entities (are key people/brands/tools explicitly named?)

Your content needs to function like a library of answer modules, not a blog post. The better your passages match these full-context prompts, the more likely you are to be cited, paraphrased, or linked as a source. Think in paragraphs, not pages. Each paragraph is a potential LLM result.

Sixth Step: Add Structured Data and Semantic Markup

LLMs use structural cues and not just raw text to identify content that’s specific, trustworthy, and well-organized. Adding semantic markup helps models interpret your content more accurately.

How do you do that?
For example, use schema.org markup for:

SoftwareApplication (for tool listings)
FAQPage (for Q&A content)
Product (for pricing, features, comparisons)
Review or Rating

Example: Use @type: SoftwareApplication to describe a tool’s integrations, platforms, and compliance. This reinforces your content’s structure and relevance across both search engines and LLMs.

Seventh Step: Build Topical Density Around the Core Theme

We all wish to be true, but publishing one great page won’t be enough. LLMs favor domains that exhibit deep topical authority. You need to show coverage across the full conceptual neighborhood. Which means, more pages and more content!

How should you approach this?

Publish case studies – they are often a perfect representation of user scenarios
Link to related use cases (e.g., onboarding workflows, compliance challenges)
Publish support content for each intent cluster in your query matrix
Interlink using semantically meaningful anchor text

Example: From your org chart landing page, link to your blog posts like “HRIS integrations for org chart software” or “Org design templates for remote teams”. The more dense and relevant your content ecosystem, the more likely you’ll appear across diverse LLM prompts.

Eight Step: Extend Retrieval via External Surface Area

LLMs don’t only cite your blog. They pull from docs, community posts, forum threads, technical Q&As, product pages… Your goal is to get retrievable surfaces in as many locations as possible.

How can you do that?

Post your best passages as answers on Reddit, Quora, and StackOverflow
Include structured summaries in help docs or support articles
Get listed on review platforms (e.g., G2, Capterra) with language that mirrors real prompts
Build case studies rich with context

The more places your passages live, the more likely they’ll be pulled into LLM conversations.

Ninth Step: Track the Metrics That Matter in the LLM SEO Era

Traditional SEO KPIs were built for a web of 10 blue links and single-intent queries. In the AI Mode era, they leave you blind to what matters most: visibility without clicks, brand mentions without links, and decision influence without attribution.

While legacy SEO metrics can be still useful, they’re incomplete in today’s online world because…

You might rank #1 for a term, but still not appear in any LLM-generated answers if your content doesn’t match the full logic of the prompt.
LLMs generate zero-click answers. Your traffic may drop, even when your visibility increases.
Bounce rate and dwell time don’t reflect LLM usage at all, especially when your content is cited, but never clicked.

In LLM SEO, visibility ≠ traffic. The value is in being cited or referenced, even if there’s no click.
Let’s look at some goals and metrics.

Goal: LLM Visibility

Metric: Citation Frequency

What It Tells You: Whether your content is appearing in LLM answers (e.g., ChatGPT, Gemini, Perplexity)

How to Track It: Tools like Peec.ai, Xofu, or prompt manually testing

Goal: Brand Impact

Metric: Brand Search Volume

What It Tells You: Users saw your brand in an AI result and searched for it directly

How to Track It: Google Search Console → Branded keyword impressions & clicks

Goal: Brand Entry

Metric: Direct Traffic

What It Tells You: People typed your URL or came from “dark” AI referrals with no clear source

How to Track It: GA4 → Traffic source: “Direct” (watch for surges post-publication)

Goal: Conversion Attribution

Metric: Branded Conversions

What It Tells You: Users who already knew your brand (often LLM-driven) took action

How to Track It: GA4 or CRM: Conversion paths that start with direct or branded search

Goal: Content Suitability

Metric: Passage Retrievability Score

What It Tells You: Is your content written in modular, prompt-aligned blocks LLMs can extract?

How to Track It: Manual review; check for format: bullets, FAQs, TL;DRs, and entity inclusion

Goal: Prompt Fit

Metric: Prompt Alignment Coverage

What It Tells You: How well your content answers common user prompts with entities, constraints, and reasoning

How to Track It: Simulate prompt sets using ChatGPT or Qforia; check if your content matches real scenarios

Goal: Entity Density

Metric: Key Entity Inclusion

What It Tells You: Whether your pages include the brands, tools, standards, and use cases LLMs need to match prompts

How to Track It: Content audit: Highlight and map entity types per passage or section

Goal: Brand Surface Area

Metric: External Presence of Answer Modules

What It Tells You: How often your content (or its ideas) appears in forums, reviews, support docs, etc.

How to Track It: Search for your content/snippets across Reddit, Quora, G2, support hubs, etc.

And now let’s move on to the probably most important question that you may have: which tools for LLM SEO are a must?

Basic LLM SEO Tool Stack

Here is a streamlined set of tools to support the most important steps in LLM SEO and LLM-driven content planning:

Purpose: Entity Mapping & Ontology

Tool: ChatGPT

What It Does: Build structured ontologies and map relevant entities to your topic

Purpose: Prompt Simulation

Tool: ChatGPT

What It Does: Generate realistic, scenario-based prompts that users might ask an LLM

Purpose: Prompt Clustering & Fan-Out

Tool: Qforia*

What It Does: Automate prompt expansion

Purpose: Topic Exploration

Tool: AlsoAsked

What It Does: Visualize related follow-up questions based on real user behavior

Purpose: Brand Visibility Tracking

Tool: Peec.ai / Xofu.com*

What It Does: Analyze whether your brand appears in LLM-generated answers, track citations, benchmark competitors, and refine content based on what LLMs retrieve

*Qforia and Xofu are currently free tools

Some Final Words…

LLM SEO isn’t just about ranking pages; it’s about writing content that can be retrieved, cited, and used by language models responding to real user prompts.

This guide introduces a practical framework for:

Mapping user intent through prompt simulation
Structuring content around context-rich scenarios
Embedding entities and formatting for modular retrieval

By focusing on clarity, relevance, and structure, you too can create content that serves both users and LLMs and meets the evolving standards of AI-driven search.

Marketing is undergoing a profound transformation driven by AI. By staying informed and proactive, businesses can harness these changes to their advantage. If you’re ready to take your marketing efforts to the next level and leverage AI-driven strategies, contact Will Marlow Agency today to see how we can help.

Interested in learning more about AI, SEO or marketing in general?
Check out our previous blog posts: