RAG — Building Reliable AI Pipelines

AUTHOR INTRO I am Madhesh, a passionate developer with a strong interest in Agentic AI and DevOps. I enjoy learning new things, and I have always wanted to start writing blogs to connect with people. I chose to work on RAG because large language models (LLMs) are everywhere, and RAG adds significant power to them by providing proper context for user queries. ABSTRACT LLMs often hallucinate on domain-specific or recent data because they don’t have the proper context for user queries. Traditional LLM outputs rely solely on trained data, which may not contain up-to-date or domain-specific information. RAG overcomes these problems with strong retrieval pipelines. In this blog, I walk through designing and implementing a complete RAG pipeline using Elastic as the vector database. From ingesting documents to semantic retrieval and LLM augmentation, discover how Elastic’s vector capabilities deliver accurate, hallucination-resistant AI applications. NAIVE SEARCH (KEYWORD SEARCH) The naive way