Semantic search is fundamentally changing how AI finds and cites content - here's what we've learned optimizing for it

Let me explain the technical side of semantic search.

How it actually works:

1. Text → Vector - Content becomes numbers (embeddings)
2. Vectors in space - Similar content = nearby vectors
3. Query → Vector - Your question becomes numbers
4. Similarity search - Find closest content vectors

The key insight:

“Best running shoes for marathons” and “top footwear for long-distance races” have DIFFERENT words but SIMILAR vectors.

AI finds both when you search for either.

What this means for content:

Keyword density is irrelevant. What matters:

• Comprehensive topic coverage
• Related concepts mentioned
• Clear entity relationships
• Natural language (not keyword-stuffed)

Model architectures:

BERT, GPT, and similar transformers understand context bidirectionally. They know that “Apple” in tech content means the company, not fruit.

Context is everything in semantic search.

E-commerce perspective on semantic search:

Our implementation:

We deployed semantic search on our product catalog (50,000 SKUs):

Why it matters for AI visibility:

The same semantic understanding that powers our search powers AI systems. When ChatGPT recommends products, it’s doing semantic matching.

What we optimized:

1. Product descriptions - Comprehensive, natural language
2. Attribute coverage - All relevant details included
3. Use case mentions - “Great for X” type content
4. Category relationships - Clear taxonomy

The AI connection:

Products with rich semantic content get recommended by AI more often. We track this with Am I Cited and see direct correlation between semantic richness and AI mentions.

Intent is the heart of semantic search. Here’s the framework:

Intent categories:

Why this matters for AI:

AI systems classify queries by intent before selecting sources. If your content doesn’t match intent, it won’t get cited.

The mismatch problem:

Product page trying to answer “what is X” = wrong intent match Educational content for “buy X” query = wrong intent match

How to optimize:

Create DIFFERENT content types for different intents around the same topic:

• Blog post for informational
• Comparison page for commercial
• Product page for transactional
• FAQ for specific questions

Cover the intent spectrum, not just keywords.

Technical implementation for semantic optimization:

Structured data helps:

Schema markup makes semantic relationships explicit:

{
  "@type": "Product",
  "name": "Marathon Running Shoe Pro",
  "category": "Athletic Footwear",
  "isRelatedTo": [
    {"@type": "Thing", "name": "Long Distance Running"},
    {"@type": "Thing", "name": "Marathon Training"}
  ]
}

Entity optimization:

Use consistent terminology:

• Define your primary entity clearly
• Reference related entities by name
• Use same terms across your site

Content structure:

AI systems parse structure:

• Clear headers (H1 → H2 → H3 hierarchy)
• Lists for enumerable items
• Tables for comparisons
• FAQs for questions

The measurement:

We analyze content with embedding similarity:

• Compare your content vector to ideal answer vector
• Closer = more likely to be cited
• Gap analysis reveals what to add

Local search is heavily semantic now:

Old local search: “pizza place north vancouver” → exact match results

Semantic local search: “somewhere good to eat after hiking quarry rock” → understands:

• Location context (North Vancouver area)
• Activity context (post-hike = hungry, casual)
• Food preference (unspecified = show variety)

How to optimize:

Include semantic context in local content:

• Nearby landmarks and activities
• Use cases for your business
• Local terminology and references
• Related local entities

Example content optimization:

“Our North Vancouver pizza restaurant is just 10 minutes from Quarry Rock trailhead. After your hike, enjoy wood-fired pizza…”

This semantic context helps AI recommend you for relevant local queries.

Results:

Pages with local semantic context: 3x more AI citations for local queries.

Quality matters more in semantic search:

Why keyword strategies could hide bad content:

Old optimization: Stuff keywords → rank → get traffic → hope for conversions

Bad content could rank if keywords matched.

Why semantic search exposes bad content:

Semantic systems understand:

• Is this content comprehensive?
• Does it actually answer the question?
• Are the claims supported?
• Is it coherent and well-written?

The quality signals:

Our experience:

We rewrote 50 pages focusing on quality, not keywords. Traffic increased 40% despite no keyword changes.

Semantic search rewards genuine quality. There’s no shortcut.

How semantic search works in AI answer systems (RAG):

The RAG process:

1. User query received
2. Query embedded (converted to vector)
3. Vector database searched (semantic match)
4. Top relevant chunks retrieved
5. LLM synthesizes answer from chunks
6. Response includes citations

What this means for content creators:

Your content competes in vector space. The question isn’t “do you have the keyword?” It’s “is your content semantically closest to the ideal answer?”

Optimization implications:

• Chunk-friendly content (clear sections, complete thoughts)
• Semantic richness (cover related concepts)
• Citable format (clear claims, supporting evidence)
• Source credibility (author, publication, expertise)

The competition:

You’re not competing against other pages for keywords. You’re competing for semantic proximity to user questions.

The most semantically relevant content wins, regardless of traditional SEO signals.

Fantastic discussion. Here’s my synthesis:

The Semantic Search Optimization Framework:

Mindset shift:

• From: “What keywords should I target?”
• To: “What question am I answering comprehensively?”

Content principles:

1. Cover topics thoroughly, not just keywords
2. Use natural language variations
3. Match user intent precisely
4. Include related concepts and entities
5. Structure content for parsing

Technical implementation:

• Schema markup for explicit relationships
• Clear content hierarchy
• FAQ sections for question matching
• Consistent entity terminology

Quality requirements:

• Genuine expertise
• Accurate information
• Clear, readable writing
• Up-to-date content

Measurement:

• AI citation tracking (Am I Cited)
• Query variation rankings
• Intent match analysis
• Content quality audits

The bottom line:

Semantic search means AI systems understand meaning, not just words. Optimize for meaning by creating genuinely useful, comprehensive content.

The era of keyword tricks is over. The era of quality content is here.

Thanks everyone for the incredible insights!