embedding models

The Core of RAG Systems: Embedding Models, Chunking, Vector Databases

In the age of large language models (LLMs), Retrieval-Augmented Generation (RAG) has emerged as one of the most powerful approaches for building intelligent applications. Whether you’re creating a chatbot, a document assistant, or an enterprise knowledge engine, three pillars make RAG work: embedding models, chunking, and vector databases. This article breaks down what they are,…

Retrieval-Augmented Generation (RAG) enhances LLM text generation using external knowledge

Editor3 months ago6 days ago09 mins

Retrieval-Augmented Generation (RAG) enhances LLM text generation by incorporating external knowledge sources, making responses more accurate, relevant, and up-to-date. RAG combines an information retrieval component with a text generation model, allowing the LLM to access and process information from external databases before generating text. This approach addresses challenges like domain knowledge gaps, factuality issues, and hallucinations often…