Vector embeddings explained: how AI compares content

What are vector embeddings?

Vector embeddings are numerical representations of text, images or other data in a multi-dimensional space. Instead of treating words as isolated symbols, AI models convert text into long sequences of numbers (vectors) that capture the meaning of that text. Two pieces of text with similar meaning receive vectors that are close together in this mathematical space, even if they use completely different words.

This concept is fundamental to how modern AI models work. When you ask a question to ChatGPT or Perplexity, the model compares the vector of your question with the vectors of billions of pieces of content to find the most relevant information. It is as if every text receives a coordinate on a map with thousands of dimensions, and the model searches for the nearest neighbors of your question.

This technical foundation directly determines which content AI models select as a source. Understanding how embeddings work helps you write better content. Our article about AEO and why it matters provides the broader context for optimizing content for AI models.

How are embeddings calculated?

Calculating embeddings happens through neural networks trained on enormous amounts of text. During training, the network learns which words and sentences appear in similar contexts. The result is a model that can convert any text into a vector, typically a sequence of 768 to 3,072 numbers depending on the model.

The process begins with tokenization: the text is divided into smaller units (tokens). The neural network then processes these tokens through multiple layers, with each layer refining the representation. The final output is a single vector representing the entire text. This vector captures not only individual words but also the relationships between words, the context and the overall meaning.

# Simplified example of vector embeddings
# (actual vectors have 768-3072 dimensions)

"dog"      = [0.82, -0.14, 0.67, 0.23, ...]
"cat"      = [0.79, -0.11, 0.64, 0.19, ...]
"car"      = [0.12,  0.85, -0.33, 0.71, ...]

# Cosine similarity (measure of resemblance):
cos("dog", "cat")  = 0.94  # very similar
cos("dog", "car") = 0.21  # very different

# The same logic applies to entire paragraphs and articles.
# AI models compare the embedding of your question
# with embeddings of billions of content pieces.

• Tokenization: text is split into sub-word units that the model can process.
• Encoding: the neural network processes tokens through transformer layers that incorporate context.
• Pooling: the output of all tokens is combined into a single vector representing the complete text.
• Normalization: the vector is scaled so comparisons are consistent regardless of text length.

Semantic search versus keyword matching

The fundamental difference between traditional search and AI-powered search lies in the use of embeddings. Traditional search engines match words: when you search for "best restaurant Amsterdam," they look for pages containing exactly those words. Semantic search based on embeddings understands meaning: it also finds pages about "top dining venues in the capital" without those exact search terms appearing.

For content creators, this has profound implications. In the era of keyword matching, it was sufficient to literally include your target keywords in your text. In the era of semantic search, your content must fully cover the meaning and intent behind search queries. A page that thoroughly covers a topic from multiple angles scores better than a page that simply repeats the same keyword.

The shift toward semantic search reinforces the importance of good readability and clear structure. AI models using embeddings understand well-structured content better and rank it higher.

How embeddings determine which content AI cites

When an AI answer engine like Perplexity answers a user question, the system goes through multiple steps. First, the question is converted into an embedding. Then this embedding is compared with an index of embeddings from millions of web pages. The pages with the highest cosine similarity (the mathematical measure of resemblance between vectors) are selected as potential sources.

This means your content must not only be relevant but also semantically close to the most common formulations of user questions. When writing about a topic, it is therefore important to use the same language as your audience. This is not about copying keywords but about employing the same concepts, relationships and terminology your audience uses when formulating a question.

1. The user question is converted into an embedding (vector representation).
2. The question embedding is compared with embeddings of millions of content passages.
3. Passages with the highest cosine similarity are selected as candidate sources.
4. A reranking model filters and orders candidates by quality and reliability.
5. The language model generates an answer based on selected passages and cites the sources.

Practical implications for your content strategy

Understanding embeddings leads to concrete advice for your content strategy. Because embeddings capture meaning rather than words, the focus shifts from keyword optimization to thematic coverage and semantic richness.

• Cover topics thoroughly rather than superficially. An in-depth article covering all facets of a subject generates a richer embedding that matches more user questions.
• Use synonyms and related terms naturally. Embeddings recognize semantic relatedness, so variation in word choice strengthens your coverage.
• Write from the user's intent. Answer not only "what" questions but also "how," "why" and "when" questions around your topic.
• Structure content with clear headings summarizing each section's topic. AI models sometimes generate embeddings per section rather than per page.
• Use concrete examples and figures. Specific information generates more distinctive embeddings than vague generalities.

It is also important to understand that embeddings are calculated at the passage level, not only at the level of entire pages. AI models like Perplexity segment web pages into smaller pieces and compare each piece separately with the user question. This means every paragraph, every section and every heading must be semantically relevant on its own.

Embedding models and their differences

Not all embedding models are equal. Different AI providers use different models, and each model has its own strengths and limitations.

OpenAI's text-embedding-3 models are widely available and commonly used for commercial applications. They offer a good balance between accuracy and speed. Google's embedding models, which underpin Gemini, are strong in multilingual content. Open-source models such as those from the Sentence-BERT family offer comparable quality and can be run locally, providing privacy advantages.

For content creators, the most important insight is that all modern embedding models follow the same core principles: they reward semantic richness, clear structure and thematic consistency. Optimize for these principles and your content will perform well regardless of which specific embedding model is used.

Summary

• Vector embeddings are numerical representations of text that capture meaning in a multi-dimensional space, enabling AI models to compare content based on meaning.
• AI answer engines compare the embedding of a user question with embeddings of millions of content passages to select the most relevant sources.
• Semantic search replaces keyword matching, making thematic coverage and natural language use more important than exact search terms.
• Content must be semantically relevant at the passage level because AI models segment pages and evaluate each piece separately.
• Optimize for semantic richness, clear structure and thematic consistency to score well regardless of the specific embedding model.

Frequently asked questions

In the world of embeddings, it is not the word you use that counts, but the meaning you convey. Write for understanding, not for keywords.