Decoding Embedding Models: Why Your RAG Is Only as Good as Your Vectors 🚀

As an AI Engineer, the first major decision you make in a RAG (Retrieval-Augmented Generation) pipeline isn't which LLM to use - it's which Embedding Model will represent your data. If your vectors are low-quality, your retrieval will fail, and even a top-tier LLM can't save a response based on the wrong context. 🏗️ What exactly is an Embedding? Embedding models take text tokens and map them into a multi-dimensional coordinate system (vectors). Dimensions: These represent the "features" the model understands. Different models represent words in vectors of different dimensions.