How to Create Content That Actually Surfaces in LLM Search in 2025

How to Create Content That Actually Surfaces in LLM Search in 2025

The search landscape has fundamentally changed. Here's how to optimize for the LLM era where AI decides if anyone sees your content at all.

The way people find information is undergoing a radical, irreversible transformation.

Gone are the days when success meant ranking high on Google's first page.

We're now firmly in the era of AI-generated answers, where large language models decide what content gets seen and what remains invisible.

This shift isn't subtle or gradual—it's a complete rewiring of discovery.

Most marketing teams are still optimizing for yesterday's search patterns while their audience has already moved on. They're tweaking meta descriptions while their potential customers are asking ChatGPT, Claude, or Google's AI for instant answers. They're chasing backlinks while their industry expertise sits unquoted and unseen.

The modern content playbook isn't just outdated—it's fundamentally misaligned with how information is consumed today.

The Rise of Zero-Click: Why Nobody's Visiting Your Website Anymore

By late 2024, a staggering 80% of consumers were regularly using AI summaries, with about 60% of searches ending without any click-through to websites [6]. This has led to a 15–25% drop in organic traffic as users increasingly rely on instant answers.

Translation?

That SEO strategy you've been refining for years? It still matters—but not in the way you think.

The goal is no longer just to rank on page one of Google.It's to appear as part of an AI-generated answer.

Your target isn't the blue link anymore—it's being the source the AI quotes when someone asks a question.

This is the new reality, and it's not reversing.

Over half of U.S. adults (52%) now use AI chatbots or LLMs for search or assistance, with more than a third using them daily [8]. Platforms like ChatGPT saw traffic surges (44% boost in a single month), while newcomers like Perplexity reached 15 million monthly users.

People are using these tools for increasingly important tasks:

• 68% use LLMs to research and summarize information

• Nearly half use them for news and weather updates

• 42% rely on them for product recommendations

The implications are clear: a massive portion of discovery is happening via AI-generated responses, not traditional search results pages.

The power shift isn't coming—it's already here.

Why Most Content Falls Flat in AI Search (And Why That Matters )

The problem isn't that your content sucks.

It's that it was built for a different era—for humans skimming pages, not machines extracting answers.

Most content marketing in 2025 is:

• Too fluffy and meandering with delayed payoffs

• Missing the clear structure AI can parse

• Lacking in specific claims and evidence

• Trying to tease the reader rather than inform directly

• Written for keyword density, not information clarity

• Focused on conversion over utility

Which means when someone asks ChatGPT, Claude, or Google's SGE a question in your industry, your competitors get quoted instead of you.

Your expertise never surfaces. Your brand never appears. And all those content calendar meetings? Wasted.

But here's the good news: AI-driven traffic, while still a small portion of overall traffic, is already showing significantly higher engagement metrics. Visitors arriving via AI chatbots spend 10.4 minutes per session (versus 8.1 minutes via Google) and view more pages [6].

In other words: AI referrals might be worth more than regular search traffic. They're pre-qualified, highly engaged, and often deeper in the decision process.

And unlike the Google traffic apocalypse that publishers have feared for years, this isn't a zero-sum game. By optimizing for AI visibility, you're not cannibalizing your traditional search traffic—you're opening up an entirely new channel of discovery.

Understanding How LLMs Choose What to Quote

Before diving into tactics, let's understand how LLMs determine what content to surface.

While exact algorithms vary, most generative search platforms use a combination of traditional search signals and AI-specific evaluations.

Topical Relevance to the Query

At the most basic level, your content needs to directly address the user's question.

LLMs (especially those using retrieval-augmented generation) retrieve passages that have strong semantic overlap with the query.

In practice, this means having content that specifically answers common questions in your domain. If a user asks, "What's the best CRM software for small B2B businesses?", an AI will look for content that explicitly talks about CRMs for small B2B companies, not just generic CRM mentions.

Contextual & Semantic Relevance

LLMs don't just do keyword matching; they understand context.

They look at the surrounding text and overall topic of your content.

A well-focused article on "CRM software for small businesses" is more likely to be picked in full or in part than a broad, meandering article that touches superficially on many topics.

The AI wants to be confident that the snippet it pulls is on-point.

Authority Signals

AI platforms aim to avoid spreading misinformation, so they give preference to sources with credibility. Signals of authority include:

• Being widely cited or referenced elsewhere

• Having a strong brand or domain reputation

• Demonstrating expertise in the content

Interestingly, according to a 2025 SEO report, backlinks themselves may play a smaller direct role in AI search rankings than they do in classic SEO – even an unlinked brand mention can contribute to your perceived authority with LLMs [4].

The underlying logic is that if your brand or content is frequently talked about (even without a link) on reputable sites, the AI likely "knows" about it from its training data and will consider it a known good source.

Clarity and Readability

If an AI can't easily parse your content, it's less likely to use it. Period.

Content that is clearly written and logically structured is much easier for the AI to digest.

One study found that even though these models can handle complex text, they prefer not to wade through disorganized content. Similarly, if your writing is convoluted or filled with fluff, the model might overlook it in favor of a source that states the point more succinctly [1].

Think of how an AI "skims" content: it might grab one or two sentences to answer a query. Those sentences need to be self-contained and clear.

The New Rules for Content That Gets Quoted (Not Just Clicked )

All that information is great, but how do you actually take advantage?

Let's cut through the noise. Here's what actually works for LLM optimization:

1. Structure Everything for Machine Legibility

LLMs love content that's easy to parse and extract.

That means:

Clear Heading Hierarchy

Use proper H2, H3, H4 tags that signal topic shifts. Make them descriptive and focused on what the section covers.

For instance, a section titled "How LLMs Determine Content Quality" is better than the clever but vague "The Brains Behind the Bots" [1].

Chunked Information

Break content into digestible sections:

• Short paragraphs with one idea per paragraph

• Bulleted or numbered lists (like the one you're reading)

• Tables for comparison data

• Blockquotes for important statements or expert insights

Summary Elements

Add elements that provide quick context:

• Table of contents at the beginning

• "Key Takeaways" boxes

• TL;DR sections

• Executive summaries for longer content

Research shows that starting long articles with a summary paragraph or bullet list of key takeaways acts as a cheat-sheet for both readers and AI. Often, an LLM might pull from a summary to directly answer a broad question [9].

Do this now: Review your top-performing content. Can a machine easily identify where one topic ends and another begins? If not, restructure with clear headers and summary sections.

2. Adopt the Question-Answer Format

Many people interact with AI by asking questions in natural language.

So frame your content the same way:

Question-Based Headings

Use headings phrased as questions people actually ask:

• "How do I optimize my website for AI search?"

• "What is the difference between traditional SEO and LLM optimization?"

• "When should I use schema markup for FAQ content?"

Direct Answer Paragraphs

Follow each question with a concise, complete answer that could stand alone if quoted. The first paragraph after a question heading should directly answer that question.

Complete FAQ Sections

Add an FAQ section at the end of posts covering:

• Common follow-up questions

• Objections or concerns

• Specific use cases or edge cases

Structuring content in a Q&A format maps perfectly to how retrieval-augmented LLMs find specific answers [1]. The closer your text matches the intent and wording of user questions, the more likely it'll be surfaced.

Search engines using AI (like SGE) will often cite the specific section of a page that provided the answer. Headings act as signposts – an AI can quickly identify which section of your page is relevant to a particular sub-question [4].

3. Leverage Data, Evidence and Expert Attribution

LLMs favor content that includes specific facts, figures, and evidence:

Recent Statistics

Always include up-to-date statistics with proper attribution.

For example:

• "According to a 2025 study by Search Engine Journal, visitors from AI chatbots spent 10.4 minutes per session versus 8.1 minutes from traditional search [6]."

When a statistic appears in your content, make it stand out:

• Use bold formatting for key numbers

• Create callout boxes for important data points

• Consider charts or visualizations with proper alt text

A 2025 LLM optimization guide noted that AI models gravitate towards concrete data when formulating answers [1]. Specific numbers and facts lend authority to an answer.

Expert Quotes and Third-Party Validation

Include quotes from:

• Industry experts

• Your own team's experts (with credentials)

• Customers or users (for social proof)

As Kelsey Libert notes, the future of SEO in the AI era is about "mentions, authority, and relevance" – producing compelling, valuable content that earns coverage and trust across the web, rather than focusing narrowly on old tricks like link building or keyword density [3].

Citations and References

Always cite your sources, whether they're:

• Academic studies

• Industry reports

• Expert opinions

• Competitor analyses

By showing your work, you signal to AI systems that your content is research-backed and trustworthy.

4. Perfect Your Content's Tone and Accessibility

AI models are trained on everyday language. Overly stiff or jargon-heavy content is harder for them to interpret correctly:

Natural, Readable Language

Write in a conversational but professional tone:

• Use active voice

• Keep sentences reasonably short

• Avoid unnecessarily complex vocabulary

• Explain technical concepts clearly

A 2025 guide specifically recommended writing for a smart 8th-10th grade reading level unless your audience/subject demands otherwise – that tends to align with how AI presents info to a general user [1].

Context and Transitions

Guide the reader (and the AI) from one idea to the next:

• Use clear transition phrases

• Explain how concepts connect

• Provide context for statistics or claims

Don't mistake conversational for superficial. AI platforms aim to prevent misinformation, giving strong preference to sources with demonstrated expertise and credibility.

Avoid Promotional Language

Keep marketing speak to a minimum. AI models are designed to detect and avoid overly promotional content when assembling answers [5].

If your content is too salesy ("Our product is the best ever!!!") or clickbait-y ("You won't believe what happened next"), the AI might exclude it in favor of something more straightforward.

5. Implement Freshness and Maintenance Signals

Content that appears current and well-maintained gets preference:

Explicit Update Signals

Make freshness obvious:

• Add "Last Updated" dates to your posts

• Include phrases like "In 2025..." or "As of Q1 2025..."

• Reference current events or recent developments

Even a simple note like "(Updated March 2025)" can signal that your content contains the latest information—making it more likely to be selected over competitors' older content [1].

Regular Content Audits

Establish a process to keep content current:

• Quarterly reviews of top-performing pages

• Updates to statistics and examples

• Rewrites of outdated sections

• Removal of obsolete content

Revision History

Consider adding a revision log to important resources:

• "First published: January 2024"

• "Updated March 2025: Added new statistics and revised AI tool recommendations"

• "Updated July 2025: Expanded section on schema markup"

This transparent approach signals to both users and AI that you're committed to accuracy and currency.

Technical Optimization: Making Your Site an AI Knowledge Source

Content optimization is just the beginning. You also need to implement technical elements that make your entire site appear as a trusted knowledge source to AI systems:

1. Schema Markup and Structured Data

Schema markup is your direct communication line to machines. Use it liberally and correctly:

Content-Specific Schema

• Article or BlogPosting schema on all articles

• FAQPage schema for FAQ sections

• HowTo schema for instructional content

• Product schema for product pages

Business Entity Schema

• Organization schema on your site footer or about page

• Include your logo, contact information, social profiles

• Use sameAs properties to link to official profiles

Specialized Schema Types

• BreadcrumbList for site navigation

• WebSite for site search capabilities

• Event for upcoming webinars or conferences

• Person schema for author biographies

According to Bain's research, only a slight majority of websites currently use structured data—giving those who do a competitive advantage in AI search results [7].

A study cited by SEO.ai found that structured data provides relationship clues that can significantly impact how LLMs understand and recommend your content [1].

Implementation tip: Use Google's Rich Results Test and Schema Markup Validator to ensure your markup is correct and recognized.

2. Develop Topical Authority Hubs

AI algorithms appreciate depth of expertise. Create connected content clusters that establish your site as an authoritative resource:

Pillar Content Strategy

• Develop comprehensive "pillar" pages on main topics

• Create related articles that explore subtopics in depth

• Ensure each pillar covers the topic exhaustively

Strategic Internal Linking

• Link related content with descriptive anchor text

• Create hub pages that organize content by topic

• Use breadcrumb navigation to show content hierarchy

Content Completeness

• Cover topics from multiple angles

• Address common questions and objections

• Provide both basic and advanced information

This approach signals to AI systems that you have comprehensive knowledge in specific areas, making it more likely to pull from your content for related questions [4].

3. Focus on Entity Establishment

Make sure your brand is recognized as an entity in knowledge graphs:

Online Presence Optimization

• Ensure consistent NAP (Name, Address, Phone) information across the web

• Create and verify profiles on Google Business Profile, Bing Places, etc.

• Maintain active, complete profiles on major social platforms

Entity Associations

• Connect your brand to relevant industry categories

• Associate with known entities (founders, products, partnerships)

• Use consistent descriptions across platforms

Third-Party Validation

• Get listed in industry directories and databases

• Seek mentions in relevant publications

• Pursue awards and recognitions that reinforce expertise

By positioning your brand as a recognized entity, you "automatically support generative chatbots" because it helps them retrieve factual information about your company—reducing AI hallucinations when someone asks about your company [4].

4. Optimize for AI Crawlability

Make it as easy as possible for AI-related crawlers to index your content:

Technical SEO Basics

• Submit sitemaps to Google Search Console and Bing Webmaster Tools

• Ensure fast page load speeds and mobile responsiveness

• Implement canonical tags to prevent duplicate content issues

Content Accessibility

• Avoid putting critical text in images (AI can't read them)

• If you use PDFs, ensure they're text-based, not scanned images

• Consider HTML alternatives for downloadable resources

AI Crawl Directives

• Unless you have specific reasons, don't block AI crawlers like GPTBot

• Be cautious with robots.txt exclusions

• Consider the implications of the "nopublish" meta tag proposal

Bain's report bluntly stated, "Forget PDFs and gated content – they're relics in an AI-driven ecosystem" [7]. This might be painful for lead-gen strategy, but you can find new ways to capture leads rather than locking up all your knowledge.

Real-World Examples: What Works in Practice

Let's examine some formats and approaches that have proven effective for LLM optimization:

1. The Comprehensive "How-To" Guide

Imagine a guide titled "How to Optimize Your Website for AI Search – 10 Key Steps". This article would:

• Start with a brief introduction and a TL;DR bullet list of the 10 steps

• Use numbered H2 headings for each step (e.g., "1. Use FAQ Sections to Answer Common Questions")

• Follow each heading with a concise explanation

• Include relevant statistics with clear attribution

• Feature expert quotes to add credibility

• Use a neutral, informative tone throughout

This format hits all the marks: it's structured, contains data points, breaks down complex topics, and is written in a way that an AI could easily extract pieces as needed [5].

2. The Authority-Building Resource Hub

Consider a cybersecurity site that publishes a detailed annual report: "The 2025 Data Breach Landscape: Stats and Insights."

This 3,000-word report would include:

• Sections for different industries, each with charts and statistics

• References to reputable sources like Verizon's Data Breach Report

• Clear, bolded statements of key facts (e.g., "There were 1,862 reported data breaches in 2025")

• Expert commentary and analysis

This approach establishes the site as an authoritative source in its domain. When questions about data breaches come up, the AI is more likely to pull from this site's content [7].

3. The Strategic Knowledge Base or Glossary

A B2B SaaS company might create a comprehensive glossary of industry terms. Each term would be:

• Published as its own page with a clear definition

• Structured with FAQ segments

• Written in a neutral, educational tone

• Internally linked to related terms

This approach makes the company a go-to reference for definitions and explanations in its industry, increasing the likelihood of being cited by AI for definitional queries [9].

4. The Data-Driven Case Study

Case studies with quantifiable outcomes make excellent fodder for AI references:

• Strong, specific headlines (e.g., "How Company X Increased Conversions by 46% Using AI-Optimized Content")

• Clear breakdown of the challenge, solution, and results

• Specific metrics and data points

• Real quotes from stakeholders

When users ask about specific tactics or results, these case studies become perfect citation material for AI responses [2].

5. The Authority-Building Newsletter

Take The RealReal's "RealGirl" Substack newsletter as an example, they created a massively engaging piece of weekly content that not only entertains readers… but positions their brand as an industry thought leader [2].

This type of content works well because it:

• Provides authentic, story-driven insights

• Establishes expertise beyond product promotion

• Creates a consistent body of knowledge on a topic

• Builds the brand as a trusted voice in the industry

Measuring Success in the Zero-Click Era

Traditional metrics won't capture your full impact in an AI-driven world. You need new KPIs and approaches:

1. Visibility Metrics

Track how often your content appears in AI responses:

• Brand mentions in AI outputs (even without clicks)

• Share of voice in AI answers for key topics

• Citation frequency compared to competitors

This requires manual testing or specialized tools that monitor AI outputs for specific queries [7].

2. Traffic Quality Indicators

Focus on engagement metrics for AI-referred traffic:

• Time on site from AI referrals

• Pages per session from AI sources

• Conversion rates from AI-driven visits

Research shows AI-referred traffic can be significantly more engaged than traditional search traffic, so tracking these visitors separately is crucial [6].

3. Brand Impact Assessment

Measure how AI visibility affects broader brand metrics:

• Brand search volume trends

• Direct traffic growth

• Brand mention frequency across the web

• Survey data on how people discover your brand

Bain's research suggests marketers should redefine success in light of zero-click search: track impressions and mentions in AI results, not just clicks and conversions [7].

4. Content Effectiveness Analysis

Evaluate which content formats and approaches drive AI visibility:

• Monitor which pages get cited most frequently

• Identify common elements in high-performing content

• Test different structures and formats

This analysis helps refine your LLM optimization strategy over time.

How Averi Transforms Your Content for AI Visibility

AI-optimized content requires precision, expertise, and consistent execution—exactly what Averi was built to deliver.

Our platform combines AI-powered insights with expert execution to ensure your content stands out in the LLM era.

AI + Human Expertise: The Winning Combination

At Averi, we reject the false choice between AI automation and human creativity.

The brands that win in LLM search need both:

• AI-Driven Analysis to identify patterns, optimize structure, and generate LLM-friendly formatting

• Expert-Led Execution to infuse content with authority, nuance, and the human touch that AI respects

This is precisely what makes Averi different. We're not just another AI content tool that generates generic outputs.

We're an AI-powered execution platform that connects you with real marketing experts who know how to optimize for LLM visibility.

The Averi Approach to LLM-Optimized Content

Here's how Averi helps you create content that consistently surfaces in AI-generated answers:

1. Strategic Content Planning

Our AI analyzes your industry, competitors, and target audience to identify:

• High-value questions AI users are asking

• Content gaps where you can establish authority

• Technical optimization opportunities for maximum visibility

Then, we match you with content strategists who specialize in your industry to build a comprehensive LLM optimization plan.

2. Structure-First Content Creation

Averi's platform enables you to:

• Build content with AI-friendly architectures baked in

• Create comprehensive FAQ sections that directly answer user questions

• Implement schema markup without technical headaches

• Structure headings and subheadings for maximum AI extractability

Our content experts ensure every piece follows LLM best practices while maintaining your unique brand voice.

3. Expert Network Access

Tap into Averi's ecosystem of vetted experts who specialize in:

• AI-optimized content strategy

• Schema markup implementation

• Technical SEO for LLM visibility

• Data analysis and visualization for compelling evidence

These specialists can execute your LLM optimization strategy on demand—without the overhead of hiring full-time or the uncertainty of random freelancers.

4. AI-Powered Measurement and Optimization

Averi helps you:

• Track your content's performance in AI-generated answers

• Identify which structures and formats drive the most visibility

• Analyze competitor content that's frequently cited by LLMs

• Continuously refine your approach based on real-world results

Real Results, Not Just Strategies

While other platforms might give you AI-generated content suggestions, Averi delivers end-to-end execution:

"With Averi, we restructured our entire content hub for LLM visibility in just three weeks. Our citation rate in AI answers increased by 78%, and we're seeing significantly higher engagement from AI-referred traffic." — Marketing Director, B2B SaaS Company

"The combination of AI analysis and expert execution helped us transform our product pages to be more LLM-friendly. Now our products are showing up in AI shopping recommendations where they were previously invisible." — E-commerce Brand Manager

Implementation: Your 90-Day LLM Optimization Plan

Here's a practical approach to implement these strategies in the next 90 days:

Days 1-30: Audit and Foundation

1. Baseline Assessment

   • Document current search traffic and conversion metrics

   • Record rankings for key terms

   • Test 25 common industry questions across different AI platforms to see if your content appears

2. Content Audit

   • Identify your top 20 traffic-driving pages

   • Analyze their structure, headings, and format

   • Score each on LLM-friendliness based on criteria above

3. Technical Foundation

   • Implement basic schema markup site-wide

   • Add FAQ schema to existing FAQ content

   • Set up tracking for AI referral sources

Days 31-60: Optimization and Structure

1. Content Restructuring

   • Reformat your top 10 pages with clear headings, FAQ sections, and summary elements

   • Add or update statistical information and expert quotes

   • Implement "Last Updated" dates and metadata

2. Schema Expansion

   • Deploy article schema on all blog posts

   • Add HowTo schema where appropriate

   • Implement organization and author schemas

3. Knowledge Base Development

   • Begin building a glossary of industry terms

   • Create FAQ hub pages organized by topic

   • Develop framework for case studies that emphasize measurable results

Days 61-90: Creation and Measurement

1. Strategic Content Creation

   • Develop 3-5 comprehensive guides in Q&A format

   • Create data-driven resources with unique insights

   • Update older content with current statistics and examples

2. Measurement Framework

   • Define AI visibility KPIs

   • Create dashboard for tracking AI-referred traffic

   • Establish process for regular AI visibility testing

3. Optimization Iteration

   • Test revised content against AI platforms

   • Analyze which formats and structures perform best

   • Document best practices for your specific industry

The Future of AI Search and Content Strategy

As we look beyond 2025, several trends are emerging that will shape LLM search optimization:

Multimodal AI and Rich Media

AI systems are increasingly able to process images, video, and audio. This means:

• Optimizing alt text and image descriptions becomes crucial

• Video transcripts should be complete and accurate

• Audio content needs text equivalents for AI consumption

More Sophisticated Entity Recognition

AI systems will get better at understanding brands, people, and concepts as entities:

• Building topical authority will become more important

• Entity relationships will influence how AI surfaces content

• Strong brand differentiation will help AI understand your unique position

Direct API Feeds and Structured Content

Some companies are already exploring direct API connections to AI platforms:

• Structured content feeds could bypass traditional search

• Real-time data connections might feed directly into AI answers

• Custom integrations could prioritize authorized sources

User Feedback and Quality Signals

AI systems will increasingly incorporate user feedback on answer quality:

• Content that consistently satisfies user needs will gain preference

• User corrections or rejections might influence source selection

• Engagement with cited sources could become a signal

The future of content isn't about ranking—it's about being the source AI trusts enough to quote. Brands that adapt quickly will claim valuable territory in AI answers, while those who cling to outdated SEO tactics will find themselves invisible in this new landscape.

Don't just optimize for keywords. Optimize for being the answer.

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