Build a Production-Ready RAG Application using Elastic search

Introduction Modern AI applications require search that understands meaning, not just keywords. Traditional keyword-based search often fails when users ask natural language questions, resulting in irrelevant or incomplete answers. For instance, if a support agent queries “how to fix login errors in the mobile app,” keyword search may miss relevant internal documentation if the exact words don’t match. Retrieval-Augmented Generation (RAG) solves this problem by combining semantic retrieval with AI generation, allowing systems to understand intent and retrieve the most relevant knowledge from large document repositories. In this guide, we will build a production-ready RAG workflow using Elasticsearch, demonstrating how vectorized thinking can transform enterprise search and AI-driven applications. What is Retrieval-Augmented Generation (RAG)? RAG works by retrieving relevant documents from a vector database and sending them as context to a large language model (LLM) before generating an