Embeddings in Generative AI

🧠 Why Embeddings Matter

• Machines don't understand text—they understand numbers.

• To work with language, machines must convert words into numbers that capture meaning, context, and relationships.

• This numerical format is called an embedding.

🔍 What is an Embedding?

• An embedding is a numerical representation of text—a way for AI to process and understand language.

• It enables models to:

○ Understand meaning

○ Capture context

○ Relate words and sentences

🧩 Example Explanation

• Sentence: "I eat ice cream"

• Process:

1. Tokenization: Breaks down into smaller parts → ["I", "eat", "ice", "cream"]

2. Neural Network (e.g., Transformer) processes these tokens

3. Generates embeddings for each token — long arrays of numbers

🧠 Why Not Just One Number per Word?

• A single number like 20 can't capture:

○ Context: Same word in different situations (e.g., “great” can be happy or sarcastic)

○ Relationships: Like "ice" and "cream" going together

○ Meaning variations depending on sentence use

🤖 How Transformers Use Embeddings

• Transformers like GPT are trained on billions of words to learn how to:

○ Encode words into meaningful embeddings

○ Predict what word comes next

• Embeddings help the model:

○ Understand grammar and sequence

○ Generate accurate and relevant responses

○ Link concepts (e.g., "eat ice → cream")

🔄 Embeddings in Action

• ChatGPT generates responses by:

○ Referring to embeddings from the input prompt

○ Predicting the next word one word at a time

Using its trained knowledge to complete the sentence accurately