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The goal of this article is to break down generative AI into simple, understandable terms without all the technical jargon.

A hands-on walkthrough of embeddings and similarity search using FIFA player data. Most of us who have played around with generative AI are familiar with building a RAG (or Retrieval Augmented Generation) model. In essence, you import your source documents to create a corpus, chunk the text into smaller pieces, and send those chunks into an embedding model to generate numerical vectors. From there, your query searches the vector database for relevant chunks, which are then

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