Fundamentals

Embeddings: the cornerstone of AI & ML

Fundamentals of embeddings: what they are, how they work, why they are so powerful and how they are created.

What are embeddings

Embeddings are the cornerstone of many AI and ML applications, such as GenAI, RAG, recommender systems, encoding high-dimensional categorical variables (such as input tokens for LLMs) and more.

For example, in an RAG application, they play a pivotal role in indexing and retrieving data from the vector DB, directly impacting the retrieval step.

They are present in almost every ML field in one form or another.

Thus, having a strong intuition of how embeddings work is a powerful skill.

In this article, you will learn about the fundamentals of embeddings, such as:

• what they are
• how they work
• why they are so powerful
• how they are created.

Table of contents

1. What are embeddings
2. Why embeddings are so powerful
3. How are embeddings created?
4. Applications of embeddings

#1. What are embeddings