Getting started with AI Engineering in 2026 - Why RAG system is a good starting point! скачать в хорошем качестве

Getting started with AI Engineering in 2026 - Why RAG system is a good starting point!

Here's the truth no one tells beginners: You don't need to learn LangChain, or Agents first. The strongest starting point for AI engineering in 2026 is RAG. Hosts: Shirin Khosravi Jam (Sr. Data Scientist) -   / shirin-khosravi-jam   Shantanu Ladhwe (Head of AI/ML) -   / shantanuladhwe   👉 RAG is not just an LLM wrapper. It's search engineering, retrieval pipelines, system design, and production infrastructure - all in one. In this session, we break down exactly how to get started with AI Engineering (aka RAG first) - from zero to production-grade systems: Why companies actually use RAG (and when they don't) The real decision drivers in production Our own experiences building RAG systems at work Why RAG quietly teaches you search, recommendations, and system design A clear path from your first local RAG to agentic systems This is not a tutorial. It's an honest conversation about what actually matters if your goal is to build and ship real AI systems! 📌 Resources & Links: 1. Getting started with RAG - https://jamwithai.substack.com/p/reth... 2. Nir Diamant's repo (only 2-5 notebooks) - https://github.com/NirDiamant/RAG_Tec... 3. Local RAG system - https://github.com/jamwithai/beginner... 4. Production RAG system - https://github.com/jamwithai/producti... 5. RAG course - https://jamwithai.dev/b/ATaKy 👉 Subscribe to Jam with AI so you don't miss what's coming next: https://jamwithai.substack.com We're building these sessions to benefit as many people as possible. We want to build an informed AI/ML community - do share with others and help us reach more! ❤️ Hope this helps :)