What is Embedding in LLM

This section explains how embeddings are used in the context of Large Language Models (LLMs), where data is represented as vectors and stored in a vector database for efficient retrieval. It explores the concept of vector indexing, the various types of vectors used, and the different kinds of objects that can be embedded along with the methods used to generate those embeddings.

Why Embedding Matters in RAG Enabled LLM Applications

This section explains why embeddings are critical for Retrieval Augmented Generation (RAG) applications powered by LLMs, showing how vector representations of text, images, and audio enable semantic search, enhance retrieval accuracy, bypass context window limits, boost scalability, support domain specific fine tuning, and unlock multimodal capabilities.

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 Right Embedding Model

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.