RAG: The 2025 Best-Practice Stack, Prototype to Production скачать в хорошем качестве

RAG: The 2025 Best-Practice Stack, Prototype to Production

Greg Loughnane, Co-Founder & CEO, AI Makerspace Chris Alexiuk, Co-Founder & CTO, AI Makerspace About the Speaker: Dr. Greg” Loughnane is the Co-Founder & CEO of AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. Since 2021, he has built and led industry-leading Machine Learning education programs. Previously, he worked as an AI product manager, a university professor teaching AI, an AI consultant and startup advisor, and an ML researcher. He loves trail running and is based in Dayton, Ohio. Chris “The Wiz” Alexiuk is the Co-Founder & CTO at AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. During the day, he is also a Developer Advocate at NVIDIA. Previously, he was a Founding Machine Learning Engineer, Data Scientist, and ML curriculum developer and instructor. He’s a YouTube content creator YouTube who’s motto is “Build, build, build!” He loves Dungeons & Dragons and is based in Toronto, Canada. Abstract: While 2025 might be the year of agents for AI Engineers, it’s the year of practical RAG for enterprise and AI Engineering leaders. In other words, RAG is table stakes; it’s a best-practice. If your organization isn’t even experimenting with RAG today, you’re behind. The good news is that best-practice tools and techniques exist. That means that you and your team can pick up open-source Commercial-Off-The-Shelf (COTS) tools to build your first RAG application today. 🙋 But wait, what is the “Best-Practice RAG Application Stack?” In this event, we share the minimum viable production-ready LLM app stack for building and evaluating your next RAG Application. Then, we’ll share how to baseline it and start improving it. Finally, we’ll comment on what you should think about to ensure that it will work well within your existing enterprise production setup and for your customers or stakeholders. We’ve been testing out frameworks and tools with our students in [The AI Engineering Bootcamp](https://aimakerspace.io/the-ai-engine..., our consulting customers, and on our YouTube channel for years now. For 2025, we believe **there is a correct stack**. Join us to discover what it is, and why! We’ll explore: 🎺 Our pick for the best **orchestration framework**: LangGraph 🎺 Our pick for the best **monitoring & Visibility**: LangSmith ↗️ Our pick for the best **vector database**: QDrant 📊 Our pick for the best way to enhance retrieval out of the box: Cohere’s Rerank 📐 Our pick for the best **evaluation framework**: RAGAS 🚀 Our pick for the best *model serving* endpoint solution: Together AI 🤖 Our pick for the best *LLM* and **embedding model**: Join us live to find out! In this event, we’ll also break down the phases of moving from prototype to production in enterprise, including: *Phase I: On-Prem Demo* (POC/MVP) with Executive/VP/Director buy-In *Phase II: Refined On-Prem Demo* with Engineering buy-In *Phase III: Data Preparation & Quality Validation* with buy-in from architects, data practitioners, and security Phase IV: *Beta Testing* with customer/stakeholder buy-in Phase V: *Scaling* a User-Friendly Product with product/design buy-in And answer the question ""what happens when we need to know move from Phase I into the organization and into a Cloud Service Provider (CSP)?"" Our pick for best **Cloud Service Provider Integration**: Join us live to find out! Finally,we'll discuss the benefits of using this approach with LangGraph applications, as well as mentioning some other leading partnerships worth noting in the industry that prioritize speed into production (e.g., CrewAI on AWS). Of course, we’ll build, ship, and share a production-grade RAG application step-by-step! Join us live to dig into the details and get your questions answered, from concepts to code!