What are Embeddings?

📰 Medium · AI

Learn how embeddings represent meaning as numbers, enabling AI to understand concepts beyond keywords

intermediate Published 16 Apr 2026

Action Steps

Use word embeddings like Word2Vec or GloVe to convert text into numerical vectors
Apply embeddings to your search engine to improve semantic matching
Experiment with different embedding techniques, such as sentence embeddings or graph embeddings
Integrate embeddings with other AI technologies, like RAG pipelines or recommendation engines
Evaluate the performance of your embedding-based system using metrics like precision and recall

Who Needs to Know This

Developers and data scientists working on AI-powered search, chatbots, or recommendation engines can benefit from understanding embeddings to improve their system's ability to capture conceptual relationships

Key Insight

💡 Embeddings represent meaning as numbers, allowing AI to capture conceptual relationships and improve search, chatbots, and recommendation engines

Key Takeaways

Learn how embeddings represent meaning as numbers, enabling AI to understand concepts beyond keywords

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Title: What are Embeddings?

URL Source: https://medium.com/@paoloperrone/what-are-embeddings-87f0c667925e?source=rss------artificial_intelligence-5

Published Time: 2026-04-16T14:11:01Z

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# What Are Embeddings? How AI Represents Meaning as Numbers | Medium

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# What are Embeddings?

## The invisible math that makes AI actually understand what you mean

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You build a customer support chatbot. You load it with 3,000 help articles. A user types “I got charged twice but only ordered one thing.” The chatbot searches your knowledge base, finds nothing relevant, and responds with a generic apology.

The answer was right there. In an article called “Duplicate Transaction Resolution Guide.” Same problem, completely different vocabulary. The user said “charged twice.” The article says “duplicate transaction.” Your search matched words. The customer was describing a situation. (I’ve shipped this exact bug. Twice.)

Even with stemming, synonym dictionaries, and fuzzy matching, no keyword engine connects “charged twice but only ordered one thing” to “duplicate transaction resolution.” The gap is conceptual, not lexical. And it’s the reason every AI search system, every RAG pipeline, and every recommendation engine on the planet now depends on the same core technology.

## TL;DR

* **Embeddings are GPS coordinates for meaning.** They convert text (or images, or code) into a list o

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What are Embeddings?

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