Vad är inbäddningar i AI-sök? Hör termen hela tiden men förstår den inte

Let 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:

1. User asks: “What are good shoes for running?”
2. AI converts this to coordinates (embedding)
3. AI looks for content with nearby coordinates
4. Your content about “athletic footwear for jogging” matches
5. 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:

You don’t optimize FOR embeddings. You optimize for clear meaning.

Let 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:

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.

To your question about different AI systems:

Yes, different systems use different 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.

Common 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:

1. Cover topics comprehensively Multiple angles = richer embedding

2. Include related concepts “Project management” + “team collaboration” + “workflow” + “productivity”

3. Answer multiple questions Each question adds semantic dimension

4. Use natural language Write for humans, embeddings will follow

The embedding is the effect of good content, not a separate optimization target.

Here’s a simple test to check if your content is “embedding-friendly”:

The query variety test:

1. List 10 different ways someone might search for your topic
2. Read your content
3. Does it help answer ALL 10 variations?

Example for “project management software”:

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.

This 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!