Vector Embedding
A numerical representation of text that AI models use to understand semantic meaning.
A vector embedding is a numerical representation of text (or other data types) in a multidimensional space. AI models convert words, sentences, or entire documents into vectors (lists of numbers) that capture their meaning. Semantically similar texts are close together in this space.
How do embeddings work?
When an AI model processes your content, it's converted into a vector. When answering questions, the question is also converted into a vector, and the system searches for content vectors closest to the question vector. This is the basis of semantic search.
Embeddings and AEO
To be well "vectorized," your content must be semantically clear. Use clear language, avoid ambiguity, and ensure the main concepts are explicitly named. Content that semantically matches user questions closely is selected more often.
How a sentence becomes a vector
A simplified example of how text is converted into a vector:
Input: "Schema.org helps search engines understand your content"
Step 1: Tokenization
["Schema.org", "helps", "search", "engines", "understand", "your", "content"]
Step 2: Embedding model processes the tokens
The model (e.g., text-embedding-3-small) analyzes the
semantic relationships between all tokens in context.
Step 3: Output vector (1536 dimensions, greatly simplified here)
[0.023, -0.041, 0.089, 0.012, -0.067, 0.034, ...]
Semantically similar sentences get similar vectors:
"Structured data makes your website understandable for Google"
[0.021, -0.038, 0.091, 0.015, -0.062, 0.031, ...]
Cosine similarity: 0.94 (very similar)
"The weather in Amsterdam is sunny today"
[-0.056, 0.072, -0.013, 0.044, 0.028, -0.089, ...]
Cosine similarity: 0.12 (not similar)Practical tips: content that embeds well
- Be explicit about your topic. Name the core topic in the first sentence of each section. An embedding model bases the vector on all words, so the more clearly the topic is named, the more precise the vector.
- Use synonyms and related terms. Don't just write "SEO — bibliotheekterm" but also "search engine optimization" and "Google visibility." This increases the chance your content matches different phrasings of the same question.
- Write self-contained paragraphs. Each paragraph should be understandable on its own. RAG — bibliotheekterm systems often retrieve individual fragments, not entire pages.
- Avoid vague language. "This is important" or "there are several options" embeds poorly. Be specific: "Schema.org markup — bibliotheekterm improves the chance of rich results — bibliotheekterm by 40%."
- Structure with clear headings. Headings are weighted more heavily in many embedding systems. Make them descriptive and specific.
- Use lists for enumerations. Structured information (lists, tables) is often better vectorized than long running text.
- Avoid overly long pages without subheadings. Content is often split into chunks for vectorization. Logical sections with headings produce better chunks.
Frequently asked questions
Can I see how my content is vectorized?
Not directly. Vectors are high-dimensional (768 to 3072 dimensions) and not human-readable. However, you can use tools to test the semantic similarity between your content and specific search queries. OpenAI's Embedding API, Cohere Embed, and open-source models like Sentence-BERT provide this capability.
Does vectorization differ per AI model?
Yes. Each embedding model produces different vectors. OpenAI text-embedding-3 generates 1536-dimensional vectors, while other models use 768 or 3072 dimensions. The semantic relationships are captured similarly, but the exact vectors are not interchangeable between models.
How does language affect vectorization?
Modern multilingual embedding models (like multilingual-e5 or Cohere multilingual) can compare texts in different languages. A Dutch question can match with an English source if the semantics align. However, these models typically perform slightly better when the question and source are in the same language.
Are vectors the same as keywords?
No, and that's the crucial difference. Keywords match on exact word correspondence ("SEO tips" only finds documents with those exact words). Vectors match on meaning ("how do I improve my findability" can match with a document about "search engine optimization techniques" without the exact words matching).
Do I need to adapt my content for vector embeddings?
Not specifically for vectors, but the principles of writing well for vectors are largely the same as writing well in general: be clear, specific, structured, and explicit about your topic. Content that is highly readable for humans typically embeds well too.
