Preparing RAG pipeline for production
The article discusses key considerations for making a Retrieval-Augmented Generation (RAG) pipeline production-ready, focusing on performance, safety, and resilience. It highlights techniques like semantic caching, optimized chunking, and access control to improve efficiency and security. The author emphasizes monitoring, evaluation, and human oversight to maintain reliability and compliance in real-world deployments.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1599843) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Dmytro Levchenko Posted on Apr 30 • Originally published at levchenkod.com Preparing RAG pipeline for production #ai #webdev #cicd #rag Intro Having a working RAG that provides correct semantic answers is a great start, yet, like with every other software, the next step is to ensure the solution is safe, optimized, and keeps your business compliant.
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