Embeddings in RAG: The Key to Smarter AI Retrieval

Unlocking RAG’s Power: How Embeddings Make AI Smarter

Photo by Kevin Ku on Unsplash

If you have ever tried to learn about AI agents, you may have encountered the term “embeddings.” Let’s understand the embeddings, how they work, and how we can use this in building AI agents.

If you already have an understanding of embeddings and want to start building your first RAG AI agent, you can read this article.

What are embeddings?

Embeddings are the mathematical representations of words, sentences, or documents. This enables AI models to better understand and retrieve the relevant context without matching the exact keyword.

One of the most famous examples used to understand embeddings is the King-Queen Analogy.

embedding (king) — embedding (man) + embedding (woman) = embedding (queen)

This means that adding the embedding associated with King and Woman while subtracting Man is equal to the embedding associated with Queen. In embedding space, words with related meanings are stored closer together, so the model understands their relationships.

Key Reasons for Converting Text into Embeddings

1. Semantic Search…