What I Actually Build: AI Systems That Ship, Not Demos That Impress
The article discusses the author's experience in building production-ready AI systems rather than flashy demos. It emphasizes the importance of robust architecture and infrastructure in AI development, particularly in sensitive domains like banking and humanitarian operations. The author seeks complex projects that require real solutions rather than simple chatbots.
- ▪The author builds AI systems that run in production, focusing on Retrieval-Augmented Generation (RAG) systems.
- ▪He has experience shipping RAG systems across various domains, including banking, humanitarian operations, and eCommerce.
- ▪The author emphasizes the need for full-stack development to support AI, including backend and frontend technologies.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 96149) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Bashar Ayyash Posted on May 23 • Originally published at yabasha.dev What I Actually Build: AI Systems That Ship, Not Demos That Impress #aiengineering #rag #aibuilders #fullstack Every LinkedIn post about AI sounds the same. "Excited to leverage cutting-edge LLMs to transform your business." Cool. What does that even mean? I'll tell you what I actually do. AI Engineering — Not the Buzzword Version I build AI systems that run in production.
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