How to Create Vector Embeddings

This section outlines the key steps involved in transforming raw data into vector embeddings.

How Embeddings Works in RAG

This section presents an overview of how embeddings function within a Retrieval-Augmented Generation (RAG) enabled LLM application.

How to Choose Embedding Models

This section guides how to choose the right embedding model for RAG. It highlights key factors such as the choice between static and contextual embeddings, general-purpose versus domain-specific models, and open-source versus closed-source options. It also covers practical evaluation techniques like Mean Reciprocal Rank (MRR) and additional considerations including token limits, retrieval effectiveness, embedding dimensionality, and model size to support informed model selection.