LLM Embeddings Explained: A Visual and Intuitive Guide

This visual guide is useful because embeddings become easier to reason about when they stop being abstract vectors and start feeling geometric. That matters for RAG, clustering, recommendations, search, and model evaluation.

I would keep it as a teaching aid, not as an implementation reference. The value is intuition: what similarity means, why representation quality matters, and why retrieval errors often start before the model answers.