Lesson 103: Embedding Models

Modern AI systems do not understand text the way humans do. For a computer, words and sentences must be converted into numbers before any comparison, search, or reasoning can happen. Embedding models solve this problem by converting text into meaningful numerical vectors.

In this lesson, you will learn what embeddings are, why they are essential, how they work, and how they are used in real AI systems.

What Is an Embedding?

An embedding is a numerical representation of text where meaning is preserved. Similar words or sentences are represented by vectors that are close to each other in a high-dimensional space.

• Text is converted into numbers
• Similar meanings result in similar vectors
• Math operations can be used to compare meaning

Embeddings allow machines to work with language using mathematics.

Real-World Analogy

Imagine a city map. Places that are close on the map are physically related. Embeddings work in a similar way, but instead of physical distance, they represent semantic distance.

Words like “king” and “queen” are close together, while “king” and “banana” are far apart.

Why Embeddings Are So Important

Without embeddings, AI systems cannot compare or search text meaningfully.

• Search engines use embeddings for relevance
• Chatbots retrieve related knowledge
• Recommendation systems find similar content
• Vector databases rely entirely on embeddings

Almost every modern AI product uses embeddings behind the scenes.

How Embedding Models Work

An embedding model takes text as input and outputs a vector of numbers. Each dimension captures some semantic property learned during training.

text = "Artificial Intelligence is powerful"

embedding = embedding_model.encode(text)

print(len(embedding))
  

Here, the sentence is converted into a vector with hundreds of numerical values. The exact size depends on the model.

Measuring Similarity Between Embeddings

Once text is converted into embeddings, similarity can be measured using distance metrics such as cosine similarity.

similarity = cosine_similarity(embedding_1, embedding_2)

if similarity > 0.8:
    print("Texts are semantically similar")
  

Higher similarity scores indicate closer meaning between texts.

Sentence vs Word Embeddings

Embedding models can represent different levels of language.

• Word embeddings: Represent individual words
• Sentence embeddings: Capture full sentence meaning
• Document embeddings: Represent long texts

Modern systems prefer sentence or document embeddings for better context.

Common Use Cases of Embeddings

Embeddings power many real-world AI features.

• Semantic search
• Question answering systems
• Recommendation engines
• Clustering similar documents

Later lessons will show how embeddings work with vector databases.

Limitations of Embeddings

Although powerful, embeddings have limitations.

• They may miss subtle context
• Bias in training data can appear in vectors
• Very long documents may lose fine details

Choosing the right embedding model is critical for performance.

Practice Questions

Quick Quiz

Coming up next: Vector Databases — how embeddings are stored, indexed, and searched at scale.