Vector Databases, Embeddings, and RAG for Enterprise AI

AI provides a comprehensive overview of the modern AI development stack, focusing heavily on data representation and knowledge grounding. Specifically, they explain embeddings as context-sensitive numerical representations of data and detail how these vectors are managed by vector databases for fast similarity search. The concept of Retrieval-Augmented Generation (RAG) is introduced as a critical technique to combat Large Language Model (LLM) hallucinations by using these vector databases to retrieve external, authoritative knowledge for informed response generation. Furthermore, the texts address the need for specialized document parsing solutions over raw LLM APIs for enterprise data, discuss the required organizational and technical changes for companies to become AI-native, and introduce the Model Context Protocol (MCP) as an open standard for connecting AI agents to external data sources and tools.