What Are Embeddings in AI Search?
Learn how embeddings work in AI search engines and language models. Understand vector representations, semantic search, and their role in AI-generated answers.
8 min read
CM
Confused_Marketer
Content Marketing Manager
·
December 21, 2025
I keep seeing “embeddings” mentioned in AI search articles. I’ve read explanations but they’re too technical.
What I understand:
- Embeddings are how AI “understands” content
- They involve numbers somehow
- They’re different from keywords
What I don’t understand:
- Do I need to optimize for embeddings?
- How do they affect my content getting cited?
- Is this something I can control?
- Do different AI systems use different embeddings?
My background: Traditional SEO marketer, 8 years experience. This AI stuff feels like learning a new language.
Can someone explain embeddings in a way a marketer can actually use?
9 comments
9 Comments
TM
Technical_Made_Simple
Expert
AI Engineer turned Consultant
·
December 21, 2025Let me explain this without the math:
What embeddings are (simple version):
Imagine every piece of text can be placed on a map. Similar meanings are placed close together. Different meanings are far apart.
- “running shoes” and “athletic footwear” = close together
- “running shoes” and “medieval castles” = far apart
Embeddings are the coordinates on that map.
Why this matters for AI search:
- User asks: “What are good shoes for running?”
- AI converts this to coordinates (embedding)
- AI looks for content with nearby coordinates
- Your content about “athletic footwear for jogging” matches
- AI retrieves and potentially cites your content
Key insight: It’s not about keyword matching. It’s about meaning matching.
What this means for your content:
| Old SEO Thinking | Embedding Reality |
|---|---|
| Match exact keywords | Convey the right meaning |
| Keyword in title | Topic clearly addressed |
| Keyword density | Semantic depth |
| Synonyms for variety | Natural language about topic |
You don’t optimize FOR embeddings. You optimize for clear meaning.
PI
Practical_Implications
SEO Strategist
·
December 21, 2025
Replying to Technical_Made_Simple
Building on this with practical implications:
How embeddings change your content approach:
Before (keyword-focused): “Looking for running shoes? Our running shoes are the best running shoes for runners who need running shoes.”
After (meaning-focused): “Choosing athletic footwear for running involves understanding your gait, terrain, and training intensity. Here’s how to find the right fit…”
Why the second works better:
The second version creates a rich semantic “map location” that matches many different queries:
- “best shoes for running”
- “how to choose running footwear”
- “athletic shoe selection guide”
- “running gear recommendations”
The keyword version’s map location is narrow. Only matches “running shoes” directly.
Practical changes to make:
- Write naturally about your topic - Cover it comprehensively
- Use related concepts - Not just synonyms, but related ideas
- Answer the “why” and “how” - Not just “what”
- Build topical depth - Multiple dimensions of the topic
The result: Your content’s embedding captures more meaning, matches more queries.
RE
RAG_Explainer
AI Systems Architect
·
December 20, 2025Let me explain RAG (Retrieval-Augmented Generation) since it’s connected:
How AI search actually works:
Step 1: User asks question “What’s the best project management tool for small teams?”
Step 2: Query becomes embedding AI converts question to coordinates (vector).
Step 3: Find similar content AI searches its knowledge base for content with nearby coordinates.
Step 4: Retrieve relevant passages Your article on “project management software comparison” has matching coordinates.
Step 5: Generate answer AI uses retrieved passages to craft response, potentially citing you.
Why this matters:
| What Helps | What Hurts |
|---|---|
| Clear, focused topic coverage | Vague, general content |
| Comprehensive answers | Surface-level coverage |
| Natural, semantic language | Keyword stuffing |
| Organized, structured content | Rambling, disorganized text |
The embedding creates the match. The content quality determines citation.
You can’t control the embedding algorithm. You CAN control how clearly and comprehensively you cover your topic.
PD
Platform_Differences
·
December 20, 2025To your question about different AI systems:
Yes, different systems use different embeddings.
| Platform | Embedding Approach |
|---|---|
| ChatGPT | OpenAI embeddings |
| Perplexity | Likely similar to OpenAI |
| Google AI | Google’s embedding models |
| Claude | Anthropic’s embeddings |
What this means: Same content might be “mapped” slightly differently in each system.
But here’s the good news: The fundamental principles are the same across systems:
- Similar meanings → similar embeddings
- Clear content → better representation
- Topical depth → richer embedding
What you DON’T need to do:
- Optimize differently for each platform
- Worry about specific embedding algorithms
- Understand the math
What you DO need to do:
- Create clear, comprehensive content
- Cover your topic thoroughly
- Use natural language
- Structure content logically
This works across all embedding systems.
CM
Common_Mistakes
Content Strategist
·
December 20, 2025Common mistakes from not understanding embeddings:
Mistake 1: Over-relying on exact keywords Old thinking: “I need ‘project management software’ in my title” Reality: AI matches meaning, not just keywords
Mistake 2: Thin content “optimized” for keywords Old thinking: 500 words targeting one keyword Reality: Thin content has weak, narrow embeddings
Mistake 3: Ignoring related concepts Old thinking: Stay focused on one keyword Reality: Related concepts strengthen the embedding
Mistake 4: Repetitive content Old thinking: Repeat keyword for emphasis Reality: Adds nothing to embedding, may hurt quality signals
What to do instead:
Cover topics comprehensively Multiple angles = richer embedding
Include related concepts “Project management” + “team collaboration” + “workflow” + “productivity”
Answer multiple questions Each question adds semantic dimension
Use natural language Write for humans, embeddings will follow
The embedding is the effect of good content, not a separate optimization target.
PT
Practical_Test
Marketing Lead
·
December 19, 2025Here’s a simple test to check if your content is “embedding-friendly”:
The query variety test:
- List 10 different ways someone might search for your topic
- Read your content
- Does it help answer ALL 10 variations?
Example for “project management software”:
| Query Variation | Does Content Help? |
|---|---|
| “best project management tools” | Should be yes |
| “how to manage team projects” | Should be yes |
| “software for tracking work” | Should be yes |
| “collaboration tools for teams” | Should be yes |
| “organizing business projects” | Should be yes |
If your content only helps with 2-3 variations, it has a narrow embedding.
The fix: Expand to cover more semantic territory. Don’t add keywords - add substance that addresses those variations.
After expansion: Your content’s embedding maps to a larger semantic area, matching more queries.
CM
Confused_Marketer
OP
Content Marketing Manager
·
December 19, 2025This actually makes sense now. My takeaways:
What embeddings are (my understanding):
- AI’s way of understanding meaning, not just words
- Like coordinates on a “meaning map”
- Similar meanings = close together = matches
What this means for my content:
Stop doing:
- Obsessing over exact keywords
- Writing thin content around one phrase
- Repetitive keyword use
Start doing:
- Comprehensive topic coverage
- Related concepts and ideas
- Answering multiple angles/questions
- Natural language that covers the topic well
The mindset shift: From: “Match keywords AI might search” To: “Cover the meaning AI needs to understand”
Practical change: Before writing, list 10 ways people might ask about my topic. Make sure content addresses all of them meaningfully.
What I don’t need to worry about:
- The actual embedding algorithms
- Different embeddings per platform
- Technical optimization for vectors
Just write comprehensive, clear, helpful content. The embeddings take care of themselves.
Thanks for making this accessible!
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Frequently Asked Questions
What are embeddings in simple terms?
Embeddings convert text into numbers (vectors) that represent meaning. Similar concepts have similar numbers. This lets AI systems match your content to user queries based on meaning, not just keywords. Think of it as AI understanding ‘what you mean’ rather than ‘what words you used.’
How do embeddings affect my content visibility?
When users query AI systems, both the query and your content are converted to embeddings. If the meanings are close (similar vectors), your content may be retrieved and cited. This is why semantic clarity and topical relevance matter more than keyword matching.
Do I need to optimize for embeddings specifically?
Not directly. You can’t control how your content is embedded. But you can ensure your content has clear, semantically rich language that accurately represents your topic. Well-written, comprehensive content naturally creates better embeddings than thin or keyword-stuffed content.
What is RAG and how do embeddings fit in?
RAG (Retrieval-Augmented Generation) is how AI finds and uses external content. It works by: 1) Converting user query to embedding, 2) Finding content with similar embeddings, 3) Using that content to generate answers. Understanding this helps explain why topical relevance drives AI citations.
