The original title is "AI Fundamentals for Developers: Tokens, Vectors and Embeddings"

Short summary

A beginner-friendly explainer covering the core concepts developers need to understand LLMs: tokens, vectors, and embeddings. It walks through how text is split into tokens by tokenizers, converted to numerical vectors, and transformed into embeddings that capture semantic meaning. The article connects these fundamentals to practical applications like semantic search and RAG.

• •Tokens are subword text pieces; tokenizers split text differently across models
• •Vectors are lists of numbers; embeddings are vectors trained to capture semantic meaning
• •Embeddings power semantic search, RAG, and recommendations by measuring conceptual distance

Generated with AI, which can make mistakes.