An Engineering-grade breakdown of RAG Pipeline

<h2> <strong>WHAT — Definition of a RAG Pipeline</strong> </h2> <p><strong>Retrieval-Augmented Generation (RAG)</strong> is an architecture where an LLM does not rely only on its internal parameters.<br> Instead, the system <strong>retrieves relevant external knowledge</strong> from a vector store and <strong>augments</strong> the LLM’s prompt with that knowledge before generating an answer.</p> <p><strong>Formula:</strong><br> </p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code>Answer = LLM( Query + Retrieved_Knowledge ) </code></pre> </div> <p>RAG is essentially <strong>LLM + Search Engine + Reasoning Layer</strong>.</p> <h2> <strong>WHY — Why RAG Exists (The Core Motivations)</strong> </h2> <h3> <strong>1. LLMs hallucinate because they guess when uncert