AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I was working on a complete SEO overhaul for an e-commerce Shopify client. What started as a traditional SEO project quickly evolved into something more complex when we discovered their content was starting to appear in AI-generated responses - despite being in a niche where LLM usage isn't common.
Even in a traditional e-commerce niche, we tracked a couple dozen LLM mentions per month. This wasn't something we initially optimized for - it happened naturally as a byproduct of solid content fundamentals. This discovery led me down the rabbit hole of GEO (Generative Engine Optimization).
Here's what I learned: LLM mention SEO is fundamentally different from traditional SEO. While Google crawls pages, LLMs consume content in chunks and synthesize answers from multiple sources. This means your content strategy needs to work on both levels.
In this playbook, you'll discover:
What LLM mention SEO actually is and why it matters now
The real difference between traditional SEO and GEO optimization
My tested framework for optimizing content for both search engines and AI models
Practical tactics that actually move the needle (tested on real clients)
Common mistakes that kill your LLM visibility
If you're still optimizing only for Google while your competitors are getting mentioned by ChatGPT and Claude, you're missing a massive opportunity. Let me show you what I learned from the trenches.
Industry Reality
What most SEO 'experts' are getting wrong about AI optimization
The SEO industry is buzzing about "GEO" (Generative Engine Optimization) like it's some revolutionary new discipline. Every guru is suddenly an expert, pushing complex frameworks and expensive tools. But here's what most of them are getting wrong.
The conventional wisdom says:
Create "AI-optimized" content with specific prompts in mind
Focus on question-answer formats exclusively
Abandon traditional SEO for AI optimization
Use special "LLM-friendly" content structures
Optimize for specific AI models individually
The problem? Most of this advice comes from people who've never actually tracked LLM mentions or worked with real business websites. They're treating GEO like it's completely separate from traditional SEO, when the reality is much more nuanced.
Through conversations with teams at AI-first startups like Profound and Athena, I realized everyone is still figuring this out. There's no definitive playbook yet. The "experts" selling courses are often just repackaging basic content principles with AI buzzwords.
What we do know is this: the foundation hasn't changed. LLM robots still need to crawl and index your content. Quality, relevant content remains the cornerstone. Traditional SEO best practices are your starting point, not something to abandon.
The real opportunity isn't in choosing between SEO and GEO - it's in understanding how they work together.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My client was a traditional e-commerce Shopify store selling physical products. Nothing fancy, nothing AI-related. We were focused on classic SEO: product page optimization, collection pages, blog content for long-tail keywords. Standard stuff.
But something weird started happening. During our monthly analytics reviews, I noticed traffic coming from sources I couldn't easily attribute. People were landing on product pages and blog posts with referral patterns that didn't match typical Google search behavior.
That's when I started digging deeper. I began manually testing searches in ChatGPT, Claude, and Perplexity related to our target keywords. Our content was being mentioned. Not every time, but consistently enough to matter.
The breakthrough moment came when I realized this wasn't random. The content that LLMs were citing had specific characteristics:
Each section could stand alone as a complete thought
Information was structured logically with clear hierarchies
We used factual, specific language rather than marketing fluff
Topics were covered comprehensively, not superficially
But here's the kicker: we hadn't done anything special for AI optimization. We were just following solid content fundamentals. The LLM mentions were a byproduct of good SEO, not a separate strategy.
This led me to a crucial realization: instead of abandoning traditional SEO for shiny new GEO tactics, I needed to layer GEO principles on top of strong SEO fundamentals. The businesses succeeding weren't choosing between Google and AI - they were optimizing for both.
Here's my playbook
What I ended up doing and the results.
After discovering our accidental LLM success, I developed a systematic approach for future clients. Instead of treating GEO as a replacement for SEO, I built it as an enhancement layer.
Layer 1: Traditional SEO Foundation
First, I made sure the basics were rock solid. Keyword research, on-page optimization, technical SEO, content structure. This isn't optional - it's the foundation everything else builds on. LLMs still need to crawl and index your content, so traditional discoverability rules apply.
Layer 2: Chunk-Level Optimization
Here's where it gets interesting. I restructured content so each section could stand alone as a valuable snippet. Instead of building content like a flowing narrative, I created self-contained blocks of information.
For example, instead of writing "As mentioned above, this approach also helps with..." I'd write "This approach helps with..." Each paragraph or section needed to make sense without the context of surrounding text.
Layer 3: Answer Synthesis Readiness
LLMs don't just copy content - they synthesize information from multiple sources. So I optimized for logical structure that made our information easy to extract and combine with other sources.
This meant:
Clear topic sentences that state the main point upfront
Logical progression from general to specific
Factual accuracy and clear attribution when citing sources
Comprehensive coverage of topics rather than surface-level treatment
Layer 4: Multi-Modal Content Integration
I started incorporating charts, tables, and structured data not just for users, but because LLMs can better process information when it's organized visually. A well-structured comparison table gets mentioned more often than paragraph text covering the same information.
Layer 5: Continuous Testing and Monitoring
Unlike traditional SEO where you wait months for results, LLM optimization can be tested immediately. I developed a routine of manually testing our target keywords across different AI platforms weekly, tracking which content gets mentioned and how.
The key insight: this layered approach improved both traditional SEO and LLM visibility. Better content structure helped Google understand our pages while making them more useful to AI models.
Foundation First
Traditional SEO remains your starting point, not something to abandon for AI optimization.
Chunk Thinking
Restructure content so each section stands alone as valuable, complete information.
Testing Rhythm
Weekly manual testing across AI platforms reveals what content actually gets mentioned.
Synthesis Ready
Optimize for how LLMs combine information from multiple sources, not just extraction.
The results weren't immediate, but they were measurable. Within three months of implementing the layered approach:
LLM Mention Tracking: We went from occasional mentions to consistent visibility across ChatGPT, Claude, and Perplexity for our target keywords. The couple dozen monthly mentions became more predictable and targeted.
Traditional SEO Impact: Interestingly, our Google rankings improved too. The content restructuring that helped LLM visibility also improved user engagement metrics and search performance.
Traffic Quality: Visitors coming through LLM-influenced searches showed higher engagement rates. They spent more time on pages and had lower bounce rates, suggesting the content better matched their intent.
Unexpected Discovery: Content that performed well in LLM mentions often became our top-performing traditional SEO content too. The principles that make content useful to AI models also make it more valuable to human readers.
But here's the most important result: we didn't sacrifice traditional SEO performance for AI optimization. The layered approach improved both simultaneously, proving that GEO and SEO aren't competing strategies - they're complementary.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Through this experience, I learned that LLM mention optimization isn't about gaming AI systems - it's about creating genuinely useful content that serves both humans and machines.
Key lessons that changed my approach:
Foundation First: Traditional SEO isn't dead. It's the platform everything else builds on.
Chunk-Level Thinking: Content needs to work as individual pieces, not just complete articles.
Synthesis Over Extraction: Optimize for how AI combines information, not just how it finds it.
Test Early, Test Often: Manual testing reveals more than theoretical frameworks.
Quality Compounds: Good content fundamentals work across all platforms and systems.
Integration Not Replacement: GEO enhances SEO, it doesn't replace it.
Patience With Pace: The landscape evolves quickly, but solid principles remain constant.
The biggest lesson? Don't abandon what works for shiny new tactics. Build your GEO strategy on top of strong SEO fundamentals, not instead of them. The landscape is evolving too quickly to bet everything on optimization tactics that might be obsolete in six months.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies looking to implement LLM mention optimization:
Focus on use-case documentation that stands alone
Create integration guides with clear step-by-step instructions
Structure feature explanations as self-contained blocks
Test your product documentation in AI platforms weekly
For your Ecommerce store
For e-commerce stores implementing this approach:
Optimize product descriptions for standalone clarity
Create buying guides that work as individual sections
Structure comparison content for easy AI synthesis
Monitor brand mentions across AI platforms regularly