AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I was working with an e-commerce Shopify client who needed a complete SEO overhaul. 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.
Here's the thing everyone's getting wrong about LLM indexing: they're treating it like traditional SEO. They're obsessing over "optimizing for ChatGPT" while completely missing the fundamentals. I spent months figuring out what actually makes content discoverable by language models, and the answer isn't what you think.
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. But after auditing hundreds of pages and tracking LLM mentions across multiple client projects, I've developed a systematic approach that actually works.
In this playbook, you'll learn:
Why traditional SEO audits miss 80% of LLM indexing opportunities
The chunk-level thinking framework that determines LLM visibility
My 5-step content audit process for AI discoverability
Real examples of content that gets cited vs. content that gets ignored
How to layer GEO optimization on top of existing SEO without starting over
This isn't about chasing the latest AI trend. It's about understanding how content consumption is fundamentally changing and positioning your content to be found in both traditional search and the emerging AI-driven discovery layer.
Industry Reality
What everyone thinks they know about LLM optimization
The SEO industry has been buzzing about "GEO" (Generative Engine Optimization) for months now. Every agency is suddenly an expert on optimizing for ChatGPT, Claude, and Perplexity. The typical advice sounds sophisticated:
Optimize for conversational queries - Write content that answers questions in natural language
Focus on featured snippets - Since LLMs supposedly pull from Google's featured snippets
Use structured data heavily - Schema markup will help AI understand your content
Write in Q&A format - Make everything easily parseable for AI systems
Target "AI-friendly" keywords - Long-tail, question-based search terms
This conventional wisdom exists because it's an easy mental bridge from traditional SEO. Agencies can repackage their existing services with an "AI optimization" label and charge premium rates. The problem? It's mostly wrong.
LLMs don't consume content the same way search engines do. They don't crawl for fresh content daily, they don't rely on backlinks for authority signals, and they definitely don't prioritize content based on traditional ranking factors. They operate on completely different principles - understanding context, synthesizing information from multiple sources, and generating responses based on training data patterns.
The real challenge isn't optimizing for AI algorithms. It's understanding how to make your content genuinely useful for synthesis and citation in AI-generated responses. That requires a fundamentally different approach to content auditing and optimization.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
When I discovered that my client's e-commerce content was getting mentioned in LLM responses, I was honestly surprised. We weren't in a tech-forward industry. We weren't targeting AI-savvy customers. Yet somehow, we were tracking a couple dozen LLM mentions per month - and this was happening naturally, not through any deliberate optimization.
This discovery led me down the rabbit hole of understanding how LLMs actually discover and use content. I started auditing our existing content to figure out what was working and why. The traditional SEO audit tools were useless for this. Ahrefs and SEMrush can tell you about search rankings, but they can't tell you if your content is citation-worthy for AI systems.
I reached out to teams at AI-first companies to understand their approach. What I learned was eye-opening: even companies building AI products don't have this figured out yet. Everyone is experimenting, testing different hypotheses, and trying to understand the patterns.
The breakthrough came when I realized we needed to think about content consumption differently. LLMs don't read pages linearly like humans do. They break content into chunks, analyze each section's usefulness for answering specific queries, and synthesize responses from multiple sources. This meant our content audit needed to evaluate chunk-level value, not just page-level optimization.
I started documenting which pieces of our content were getting cited, how they were being used in AI responses, and what characteristics they shared. The patterns that emerged were completely different from traditional SEO success factors.
Here's my playbook
What I ended up doing and the results.
After months of experimentation and analysis, I developed a systematic approach to auditing content for LLM indexing. This isn't about abandoning traditional SEO - it's about layering a new evaluation framework on top of your existing optimization efforts.
Step 1: Chunk-Level Content Analysis
Instead of evaluating entire pages, break your content into logical sections. Each paragraph or subsection needs to be able to stand alone as a valuable piece of information. LLMs often extract specific passages to answer questions, so every chunk should be self-contained and contextually complete.
I audit each section by asking: "If this paragraph was the only thing someone read about this topic, would they understand the concept and be able to act on it?" If not, the chunk needs work.
Step 2: Answer Synthesis Readiness
LLMs excel at synthesizing information from multiple sources. Your content needs to be structured in a way that makes synthesis easy. This means clear logical flow, obvious cause-and-effect relationships, and explicit connections between concepts.
I restructured our content so each section could contribute to different types of AI responses - some sections provide definitions, others offer step-by-step processes, others give examples or case studies. This diversity makes the content more useful for synthesis.
Step 3: Citation-Worthiness Assessment
Not all content is citation-worthy. LLMs tend to reference content that is factual, specific, and authoritative. Vague marketing copy gets ignored. Specific data points, clear methodologies, and concrete examples get cited.
I audited our content for "citation triggers" - specific numbers, unique processes, original research, or distinctive perspectives that an AI would want to reference when answering related questions.
Step 4: Topical Breadth and Depth Evaluation
LLMs value comprehensive coverage of topics. Instead of thin content targeting specific keywords, they prefer resources that cover all facets of a subject. I mapped our content against the full scope of topics in our niche, identifying gaps where we needed deeper coverage.
Step 5: Multi-Modal Content Integration
While LLMs primarily work with text, they increasingly understand context from charts, tables, and structured data. I audited our content for opportunities to add visual elements that enhance understanding and provide additional citation opportunities.
The key insight? Quality content for LLMs isn't just well-written - it's genuinely useful for synthesis and citation. Every piece needs to contribute unique value that an AI system would want to reference when generating responses.
Citation Triggers
Specific data points and unique methodologies that make content reference-worthy
Chunk Architecture
Breaking content into self-contained sections that work independently
Synthesis Value
Ensuring each piece contributes to broader knowledge synthesis
Authority Signals
Building content that LLMs recognize as credible and worth citing
The results weren't immediate, but they were significant. Within three months of implementing our LLM-optimized content audit, we saw our monthly LLM mentions increase from a couple dozen to over 100. More importantly, the quality of mentions improved - we were being cited as authoritative sources rather than just mentioned in passing.
Traditional SEO metrics improved as well. Our content became more comprehensive and better structured, which helped with traditional search rankings. Page engagement increased because the content was more useful and easier to scan.
The unexpected outcome? Our content started performing better across all discovery channels - not just AI systems. When you optimize for synthesis and citation, you naturally create more valuable, comprehensive content that performs well everywhere.
What surprised me most was how this approach affected our content creation process. Instead of targeting individual keywords, we started thinking about comprehensive topic coverage. Instead of optimizing single pages, we built content ecosystems where different pieces supported and referenced each other.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons I learned from auditing hundreds of pages for LLM indexing:
Traditional SEO skills are still foundational - LLMs still need to discover your content, which means basic SEO hygiene matters
Chunk-level thinking is essential - Optimize sections, not just pages
Comprehensiveness beats keyword targeting - Cover topics thoroughly rather than targeting specific terms
Citation-worthy content has specific characteristics - Factual, specific, and genuinely useful for synthesis
The landscape is still evolving rapidly - What works today might change in six months
Quality content wins in all channels - Optimizing for LLMs improves performance everywhere
Don't abandon traditional SEO - Layer GEO optimization on top of solid SEO fundamentals
The biggest mistake I see teams making is treating this as an either/or decision. You don't need to choose between traditional SEO and LLM optimization. The best approach builds GEO strategies on top of strong SEO fundamentals.
This approach works best for content-heavy SaaS companies and educational resources. It's less effective for pure product pages or marketing copy. Focus your LLM optimization efforts on your most valuable, educational content first.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups looking to implement LLM content auditing:
Start with your knowledge base and help documentation
Focus on educational content over promotional pages
Create comprehensive guides rather than thin blog posts
Track mentions in AI responses as a new metric
For your Ecommerce store
For e-commerce stores implementing this approach:
Optimize category descriptions and buying guides
Create comprehensive product comparison content
Focus on educational content that supports purchase decisions
Build topical authority in your product categories