Giving Models Memory (Embedding & Vector Math)
What are Text Embeddings?
Learning Outcome
5
Compare keyword and embedding-based search.
4
Identify embeddings in search, RAG, and recommendations.
3
Understand how text similarity is measured
2
Learn how embeddings represent text as numbers.
1
Understand text embeddings and their purpose.
The City Map Analogy
Nearby places are close together on the map. Distant places are far apart.
Text embeddings work the same way. Each piece of text is placed in a mathematical space based on its meaning.
- Similar meanings = placed close together
- Different meanings = placed far apart
The AI does not compare only the words. It compares their meaning — this is what makes embeddings so
powerful.
Imagine a city map. Every location has coordinates.
The City Map Analogy
Nearby places are close together on the map. Distant places are far apart.
Text embeddings work the same way. Each piece of text is placed in a mathematical space based on its meaning.
- Similar meanings = placed close together
- Different meanings = placed far apart
The AI does not compare only the words. It compares their meaning — this is what makes embeddings so
powerful.
Imagine a city map. Every location has coordinates.
The map was just an easy example to help us understand the idea. Now let's break down what a Text Embedding really is.
What is a Text Embedding?
A Text Embedding is a numerical vector representation of text that captures its semantic meaning. It allows AI systems to measure similarity, search for related information, cluster documents, and perform many other NLP tasks.
In simple terms, an embedding is a list of numbers that represents the meaning of a word, sentence, paragraph, or document.
The numbers themselves aren't meaningful to humans, but together they represent the semantic meaning of the
text.
Example (simplified):
How Are Text Embeddings Created?
Similarity in Embeddings
Consider these three sentences:
- Sentence A: "I enjoy reading books."
- Sentence B: "I like reading novels."
- Sentence C: "Cars require regular maintenance."
Sentence A and B express similar ideas; Sentence C is a completely different topic. Because of this:
- Sentence A and B have similar embeddings, placed close together
- Sentence C has a very different embedding, placed far away
This enables AI to retrieve relevant information even when the exact words used are different.
Where Are Text Embeddings Used?
Where Are Text Embeddings Used?
Keyword Search vs Embedding Based Search
Semantic Search in Action
Daily Life Applications
Searching an online store for "healthy snacks" and getting results for "nutritious food" and "low-calorie
treats."
A streaming platform recommending a movie because it's similar in theme to one you already watched.
A chatbot pulling the right answer from a help document even when you phrase your question differently than the document does.
Automatically groups similar notes using embeddings
Summary
5
Embeddings power search, RAG, recommendations, and chatbots.
4
Different meanings produce vectors farther apart.
3
Similar meanings produce vectors closer together.
2
Embedding models transform text into comparable vectors.
1
Embeddings convert text meaning into numerical vectors.
Quiz
What helps find related meanings, not exact words?
A. Keyword matching
B. Text Embeddings
C. Manual tagging
D. Random selection
Quiz-Answer
What helps find related meanings, not exact words?
A. Keyword matching
B. Text Embeddings
C. Manual tagging
D. Random selection
