Key Learning Points:
- Embedding is a technique that converts words or images into numerical data that computers can process, placing similar meanings or features close together.
- This mechanism is used in familiar AI services such as search engines and recommendation systems.
- However, Embedding has its limitations—it cannot fully replicate human senses or cultural backgrounds.
How Does AI Understand the Meaning of Words?
Have you ever wondered, “How does AI understand the meaning of words?” For example, when you type “recommended cafes” into a search engine, it might show you results like “popular coffee shops nearby.” As humans, we naturally understand that “cafe” and “coffee shop” are similar in meaning. But for a computer, these are just strings of characters. To grasp their “meaning,” some special techniques are needed.
One key technology behind this is called “Embedding.”
What Is Embedding and How Does It Work?
Embedding is a method for converting information—like text or images—into numbers that computers can more easily understand. But these aren’t just random numbers. They’re designed so that items with similar meanings or features are placed close together in what’s called a “multi-dimensional space.”
For instance, the words “cat” and “dog” are both animals, so they would be located near each other in this space. On the other hand, something completely different like “cat” and “car” would be far apart.
This idea is somewhat similar to how we humans perceive things. Imagine walking through an unfamiliar town and noticing areas where there’s a train station, cafes, and bookstores all clustered together—you might get the sense that it’s a lively downtown area. Embedding works in a similar way: it expresses meanings and atmospheres as numbers and interprets their relationships through distance.
How Embedding Is Used in Our Daily Lives
This technology plays a big role in our everyday experiences. Take online shopping, for example—when you see messages like “People who bought this item also viewed these,” that’s the recommendation feature at work. Behind the scenes, Embedding is being used to calculate how similar different products are.
In large-scale language models (LLMs), entire sentences are converted into embeddings to improve search accuracy or uncover connections with new information. Techniques like vector search and RAG (Retrieval-Augmented Generation) also rely heavily on embedding.
That said, this technology isn’t perfect. Because embeddings learn from past data patterns, they may miss subtle nuances or cultural contexts that humans pick up on easily. Also, since everything is represented in multi-dimensional space—a concept hard for us to visualize—it can be difficult to understand why certain results were produced. This lack of transparency remains one of the challenges.
A Map That Represents the World of Words
Even so, thanks to embedding technology, AI has gradually become better at understanding human language and intentions. It’s an effort to treat words not just as symbols lined up on a screen but as meaningful elements—and embedding represents one of the first steps toward that goal.
The next time you use your smartphone’s search function or chat with a chatbot without giving it much thought—remember that tiny numbers are quietly working behind the scenes. And when you think of those numbers as forming a kind of “map of language,” perhaps you’ll feel a bit more connected to them.
In our next article, we’ll explore how this map created by embeddings helps us find information—in other words, we’ll take a closer look at vector search. If you’re curious, I hope you’ll continue reading.
Glossary
Embedding: A method for converting information such as text or images into numerical data that computers can understand. Items with similar meanings or features are placed close together.
Multi-dimensional space: A space made up of multiple axes (dimensions) where relationships between pieces of information can be expressed as distances. While difficult for humans to visualize intuitively, AI uses this space to determine semantic closeness between data points.
Recommendation feature: A function often seen in online shopping platforms where suggestions like “People who bought this also viewed…” appear. These recommendations are based on calculated similarities between product features.

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