Case Study: Building an AEO-Optimized AI Platform
Project Goal: Create a reference site for AI concepts that is optimized for both human readers and AI answer engines like ChatGPT and Perplexity, demonstrating Answer Engine Optimization (AEO) techniques.
The Challenge
Traditional SEO optimizes for search engine rankings. Answer Engine Optimization (AEO) optimizes for being cited in AI-generated responses. These require different approaches to content structure, markup, and information architecture.
The challenge was to create content that AI tools would recognize as authoritative and cite-worthy while remaining useful for human readers.
Scope and Constraints
To make this a manageable portfolio project, I set specific constraints:
- 5 core concept pages plus 1 applied example
- Single topic cluster (AI fundamentals)
- Plain HTML and CSS only
- No external dependencies or frameworks
- Manual schema markup implementation
These constraints forced focus on content quality and structure rather than technical complexity.
Content Strategy
Answer-First Structure
Every page follows this pattern:
- H1 with the exact question being answered
- Definition block with complete answer in first 150 words
- Answer block with immediate explanation
- Detailed sections using clear H2 headers
- FAQ section with common follow-up questions
This structure ensures AI tools find the answer immediately while giving human readers options for deeper exploration.
Topic Selection
I chose concepts that met specific criteria:
- Frequently asked questions with vague or incomplete existing answers
- Terms that connect to each other (enabling internal linking)
- Practical relevance for non-technical decision makers
- Conceptual enough to remain current (not tied to specific tools)
Writing Style
Key principles:
- Short paragraphs (2-4 sentences maximum)
- Declarative sentences, no hedging
- No marketing language or fluff
- Technical accuracy without jargon
- Examples using real scenarios
Technical Implementation
Schema Markup
I manually implemented three schema types on every relevant page:
Article Schema includes headline, description, author, publisher, dates, and word count. This helps search engines understand page content and freshness.
FAQPage Schema structures questions and answers in a machine-readable format. This enables FAQ rich results in search and helps AI tools extract specific Q&A pairs.
Breadcrumb Schema shows information hierarchy and navigation structure. This helps both users and search engines understand site organization.
All schemas were validated using Google's Rich Results Test. Manual implementation gave complete control over what information was emphasized.
Internal Linking Strategy
Every concept page links to 3-4 related concepts where they naturally connect in the content. This creates a knowledge graph that:
- Helps readers discover related information
- Signals topical authority to search engines
- Ensures no page is more than 3 clicks from homepage
- Creates no orphan pages
Footer navigation provides secondary paths to all pages for both users and crawlers.
Performance Optimization
To maximize speed and accessibility:
- Single CSS file shared across all pages
- Zero JavaScript required
- Semantic HTML5 only
- No external dependencies
- Mobile-first responsive design
Pages load in under 1 second even on slow connections.
Design Decisions
Frameworks add complexity without value for this use case. Plain HTML is fast, deployable anywhere, and gives full control over markup for schema optimization.
Schema plugins often add unnecessary markup or miss optimization opportunities. Manual implementation ensures exactly the right data is marked up in exactly the right way.
Better to have 5 excellent pages than 50 mediocre ones. This scope allowed high quality content and thorough testing within project timeline.
Conceptual pages demonstrate understanding. The applied RAG system page demonstrates ability to design production systems, making this more valuable for technical roles.
These pages transform the site from content demo into product thinking demo. They show decision-making process and strategic thinking involved in building this product.
Testing Methodology
To measure AEO effectiveness, I developed a testing protocol:
Baseline Testing
Before launch, I tested each question against ChatGPT, Perplexity, and Google AI Overview. I recorded:
- Exact answer provided
- Sources cited
- Whether those sources answered the question well
- Answer structure and completeness
Post-Launch Testing
After launch, I ran the same tests to measure if AI tools started citing this site. I compared:
- Citation rate (what percentage of answers cited my pages)
- Direct quotes vs paraphrased content
- Whether answers improved in quality
- Position in source lists when cited
Iteration Testing
For each content change, I:
- Documented the specific change made
- Waited 48-72 hours for re-crawling
- Re-tested affected pages
- Logged results to track what improvements worked
Results
Schema Validation
All pages passed Google Rich Results Test with zero errors. Article, FAQ, and Breadcrumb schemas validated correctly across all pages.
Site Structure
Achieved target information architecture:
- Maximum 3 clicks to any page
- Zero orphan pages
- Consistent internal linking (4 links per concept page)
- Clear navigation hierarchy
AEO Testing Results
After deployment, I observed:
- Structured content improves citation rate
- FAQ schema increases appearance in direct answers
- Answer-first structure improves quote selection
- Clear section headers help AI tools extract specific information
Key Findings
Structure Beats Volume
Five well-structured pages outperform fifty poorly organized ones. Clear hierarchy, consistent formatting, and front-loaded answers make content more discoverable and more useful.
Schema Implementation Is Critical
Structured data significantly impacts how search engines and AI tools understand content. Manual implementation is time-consuming but provides complete control.
Internal Linking Creates Authority
Connecting related concepts helps establish topical authority. Every concept should link to at least 3-4 related concepts where connections are natural.
Answer-First Works
Putting complete answers in the first 150 words serves both human readers and AI systems. Readers get quick answers, AI tools find quotable content immediately.
What I Would Build Next
More Applied Examples
Add 3-5 more pages showing concepts in practice. Examples: "Designing a Prompt Chain for Customer Support", "Building a Classification System with Fine-Tuning", "Vector Search Implementation Patterns".
Comparison Guides
Create pages that compare approaches. Examples: "RAG vs Fine-Tuning: When to Use Each", "Vector Search vs Keyword Search", "Single Prompt vs Prompt Chain".
Longer-Form Testing
Run 30-day tests on content variations. Test different answer lengths, section structures, and FAQ formats to measure what performs best.
Visual Diagrams
Add detailed architecture diagrams to each page showing how concepts work visually. This would improve both understanding and shareability.
Interactive Examples
Build simple interactive demos showing how concepts work in practice. Example: a prompt chain where you can see each step's output.