# Semantic Caching in Enterprise RAG: Production Architectures for Faster, Lower

<p>A few years ago, <em>"AI in your app"</em> meant a chatbot that answered FAQs. Today, it means an agent that can search, filter, compare, book, and transact - all while

<p>If you're calling an LLM API for every single user request, you're almost certainly paying for the same question more than once. Not because your users are dumb because human

<p>I had a clean theory about how RAG caches silently corrupt your answers. Then I built one, ran the numbers, and the actual failure mode was the opposite of what I expected.</p>

<h2> Why my Cursor bill looked weird </h2> <p>I was poking around my usage dashboard in Cursor and noticed a metric I'd never paid attention to before: <strong>Cache

<p>Most semantic search systems don’t fail because of embeddings. They fail because of how the system is designed around them.</p> <p>When I started building a semantic search API

<!-- SC_OFF --><div class="md"><p>Hi Reddit, we are a team of database researchers (including a PhD from MIT DB Group) and we just open-sourced an embedded vector database for

<h1> Building Production RAG Systems in .NET 10: The Complete Guide to Embeddings </h1> <h2> The Hallucination Problem </h2> <p>Your company spent $50K building an internal