LLM Optimization: A Practical Guide to Thriving in AI Platforms

This guide created by Chris McLellan, Principal marketer at Friends Electric outlines the trends reshaping the online experience and 5 specific tactics that brands can use in order to increase their visibility, citations, and sales inside AI platforms like ChatGPT, Gemini, Claude, and Perplexity. The post includes one cutting-edge, agent-related 'bonus tactic' that all brand owners need to know about.

Last updated:

• February 18, 2026

TL;DR

Today’s search engines increasingly display AI-generated summaries before traditional results. At the same time, conversational tools like ChatGPT, Gemini, and Claude provide longer, synthesized answers that allow buyers to compare options and narrow their choices without visiting multiple websites. Even complete end-to-end transactions are now beginning to happen inside these AI environments.

For many buyers, these experiences offer a faster and more convenient experience than navigating multiple sites or even searching within hosted e-commerce stores. History suggests that convenience tends to win in such situations, just ask Uber.

LLM Optimization, which combines 'AEO' and 'GEO', is a set of tactics designed to ensure your brand and products can be clearly understood, cited, and evaluated in both AI summaries and conversational answers. If they cannot, visibility, mentions, and ultimately sales may begin to decline.

Key LLM Optimization tactics:

• Use 'fan-out' based research to identify the sub-questions that AI systems generate around the human search queries

• Organize those sub-questions by persona and buying stage to determine what becomes a landing page, a section, or an FAQ, then structure for clean extraction by LLM crawlers

• Generate off-site backlinks and mentions where AI systems look for authority e.g. Reddit, podcasts, YouTube, blogs, industry guide, and local directories.

• Test ChatGPT ads (USA only at present)

• Build direct app integrations into ChatGPT

• Bonus tactic: update websites to the new WebMCP standard

• Track relevant metrics such as LLM Visibility, LLM Citations, and LLM Share of Voice

Teams that start building visibility inside large language models now will compound advantage as these platforms mature. Teams that wait will soon start to wonder where their online business has gone.

Defining Terms: Answer Engine Optimization

What is LLM Optimization?

The combination of tactics associated the emerging disciplines of AEO and GEO (see below).

What is Answer Engine Optimization?

The process associated with getting products and services mentioned and referred in the AI summaries of traditional search tools like Google Search, as well as newer AI search tools like Perplexity.

What is Generative Engine Optimization?

The process associated with getting brands, products, services, and thought leaders mentioned in the answers generated by chatbots like ChatGPT, Claude, and Gemini.

What is AI search query 'fan out'?

The process by which Large Language Models break up human search queries e.g. "What are the best hotels in Toronto?" into many sub-questions in order to create robust answers from multiple sources.

What is an 'Extractable Landing Page'?

An extractable landing page is an evolution of the traditional SEO landing page. It is built not only to rank in search, but to clearly answer specific buyer questions in a structured, machine-readable format so AI systems can extract, cite, and reference it in summaries and conversational responses. Unlike lead generation pages that prioritize form fills, an AEO landing page prioritizes clarity, precision, and extractability so it performs in both search engines and AI-driven discovery.

4 Ways that AI is Fundamentally Changing the Customer Experience

I recently wrote about AI’s impact on email marketing. But Generative AI and AI-powered search are also reshaping buyer experiences and fundamentally rewiring traditional patterns in online navigation and commerce.

Here are 4 trends to watch:

1. Evolving Discovery

Answers increasingly appear before links. Inclusion in AI-generated summaries now matters as much as traditional page ranking. AI Overviews and generative responses are absorbing informational queries and materially reducing click-through rates, shifting visibility from blue links to citations.

2. Compressed Journeys

Buyers are comparing and evaluating options inside AI platforms before visiting your site. Much of the discovery, comparison, and early qualification that once happened across multiple websites and tools is now happening in a single session in a single tool.

3. Composable Experiences

On-site and in-app bots are bypassing traditional UI and enabling dynamic, conversational experiences. Website visits are becoming more self-directed and personalized, allowing users to assemble information in real time rather than navigating fixed web pages or standard product catalogs.

4. Shifting Point of Sale

Buying is moving into AI environments in two ways. First, brands are integrating their apps and e-stores directly into chat platforms like ChatGPT. Second, agentic shoppers are starting to act on behalf of human customers to research, compare, and initiate purchases. In both cases, decisions are increasingly shaped inside AI experiences rather than traditional web searches, websites, and e-stores.

5 Key Tactics (+1 Bonus Tactic) To Thrive In Large Language Models

Tactic 1: Generate Backlinks & Mentions

Mentions and backlinks will increasingly determine whether your brand, products, or subject matter experts are cited in LLM conversations at all, so the key is to start by building brand mentions where LLM crawlers already "hunt for the truth":

• Reddit

• LinkedIn

• Quora

• Industry blogs

• Podcast episode notes

• Review platforms such as G2, Capterra, and TrustRadius

• YouTube creator videos

• Newsletters

Building authority inside LLMs requires sustained Digital PR and third-party validation.

Tactic 2: Create Fan-Out Driven Page Strategy

The goal of this tactic is to create a high-intent inventory of questions that buyers ask about your products, and LLM engines attempt to answer.

• Pick one thing you sell:

  • Identify 3 to 5 common buyer questions using:

    • Sales calls

    • Support tickets

    • Search tools

    • Reddit, Quora, YouTube, Instagram (via Meltwater or Common Room if available)

      
      

• Expand question list:

  • Enter each base question into a search demand tool such as Ahrefs or Semrush to generate a list of related search terms and question variations.

  • Enter the same base questions into Dejan' Fanout Tool (or an LLM) to generate additional AI-style question variations.

  • Add all results into one master list for the product.

• Clean question list:

  • Use an LLM to remove duplicates, standardize wording, keep buyer language

• Create product content spreadsheet:

  • Create one spreadsheet per product

  • Personas down the left

  • Awareness → Consideration → Evaluation → Decision across the top (columns)

  
  

• Map questions into spreadsheet:

  • Place each question in the correct cell

  • Group similar questions in each cell

  • Each cluster represents one clear intent at that stage

• Decide what each question cluster becomes:

  • Hub page:

    • Broad commercial intent

    • Core product or primary solution

    • Central navigation destination

  • Spoke page:

    • Narrow but still meaningful intent.

    • A specific use case, audience, comparison, or outcome.

    • Can stand alone and rank independently.

  • Section (within a page):

    • Sub-topic that supports a hub or spoke.

    • Needs explanation but not its own URL.

  • FAQ (individual question):

    • Clarification, objection, edge case.

    • Short answer.

    • Only makes sense in context of the main page.

Repeat this process for your top-performing products, strategic growth bets, or core categories. If you support many related products, run it at the category or solution level.

Some larger teams may choose to apply this at the feature level, but that requires significant content and operational capacity.

Tactic 3: Build Extractable Landing Pages

Almost any company can build (or re-build) landing pages to meet the demands of LLM crawlers. However, unlike traditional SEO landing pages, the design objective of Answer Engine Optimization landing pages is more about clean extraction of information as opposed to general education.

Below are a few practical tips for designing AEO landing pages. Note: Most AI-forward marketers, including myself, utilize AI systems to co-build AEO landing pages faster and at less cost.

• Page layout:

  • Add "Last updated" date

  • Start with a one sentence direct answer

  • Add two or three short paragraphs of deeper explanation.

    • Write each section so it stands alone when extracted out of context

    • Use bullets, short paragraphs, and tables instead of long text blocks

    • Connect each idea back to your product to teach models when you are relevant

  • Break down a unique process when applicable e.g. "Combustible Commerce is our approach to..."

  • Add “fact blocks” that include a stat, brief explanation, and source link e.g. case study

  • Embed dynamic data e.g. embedded polls

  • Add FAQ (see next)

• Page FAQs:

  • Add FAQs that support the specific sub-questions generated during fan out.

  • Include 5-7 FAQs per page.

  • Each FAQ should have an H3 or H4 heading, a one sentence direct answer, and two or three sentences of context.

  • Add "Last updated" date to FAQ section

• Page Schema:

  • HowTo schema for step by step workflows

  • Product schema for feature and pricing information

  • Article schema for explainer

  • FAQPage schema for landing page FAQs

https://www.youtube.com/watch?v=8z_KQv2vBpM

Tactic 4: Buy ChatGPT ads

Just like in the old days of Google Search, brands can now skip the line and buy their way into OpenAI by paying for OpenAI ads.

As of writing, OpenAI ads are only available in the US market. But it's safe to say that they will be available in most major markets by the end of 2026.

Tactic 5: Build ChatGPT integration

For larger ecommerce or data-rich brands, submitting their app to ChatGPT for direct integration can enable direct transactional experiences inside the AI environment.

Bonus Tactic: Adopt the WebMCP Standard

While not exactly 100% related to LLM optimization, this tactic is potentially so impactful that it warrants inclusion here. Google has recently announced WebMCP, new standard that makes websites "agent-ready", meaning AI agents can take actions on a site (like booking a flight or filing a support ticket) reliably and precisely, rather than awkwardly fumbling through a page the way current AI tools do.

WebMCP matters because as AI agents increasingly fill out forms and load online shopping carts for people, websites that speak the agent's language will deliver far better experiences than those that don't.

It's virtually guaranteed that 'Autonomous Agent Optimization' (or something like that) will be a thing by mid-2026.

Key Metrics to Track Answer Engine Optimization

Expect traditional site traffic to fluctuate as discovery shifts into AI platforms.

• AI visibility: Are you appearing in AI summaries and answers

• Branded query lift: AI exposure increasing brand searches

• Share of Voice: Relative presence vs. competitors in AI responses

• Citation frequency: How often your brand is referenced as a source

• LLM-driven conversions: Purchases, bookings, or leads initiated inside AI environments

• Paid AI campaign performance: If running AI-native ads

Tools to Measure AEO & GEO Results

• HubSpot’s AEO Grader: free tool that provides a high level visibility snapshot.

• Xfunnel: paid app to see which questions you appear for, which you do not, and which external sources AI uses instead. Recently acquired by HubSpot.

LLMs & Agents are Eating The Web. It's Critical To Stay Visible

AI platforms are basically disrupting any human activity that involves a keyboard or touch screen. This includes how buyers discover, evaluate, and choose products and services.

Large Language Models reward clarity, structure, and credibility. They surface brands that provide direct answers, appear consistently in trusted sources, and publish content that is easy to extract and cite. Brands can now also accelerate visibility through ads or direct integrations inside AI platforms.

"Does this mean my web or mobile UI is becoming less central, and that the future is more about core capabilities and delivering results?"

Yes.

"Does this mean we need to rethink the SEO landing page design we've been using for years?"

Yes.

For many buyers, AI platforms already offer a better experience than the traditional web. And if the past 20 years has taught us anything, it’s that convenience wins.

Teams that start building visibility inside Large Language Models now will compound advantage as these platforms mature.

Teams that wait will eventually wonder where their online business went.

If you'd like to discuss what this means for your brand, please get in touch.