RAG: The 2025 Best-Practice Stack
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, 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: LangChain’s LangGraph
↗️ 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
We will also review some less controversial best-practices, including:
🦙 The best open-source LLM for enterprise
🔢 Some great open-source embedding models
🍰 Top chunking strategies
🐕 Leading Advanced retrieval techniques
🔤 Dealing with structured vs. unstructured data
🔖 The importance of metadata
And we’ll even touch on the “ends” of end-to-end applications and talk about the best ways to:
💬 Construct a front-end
🛎️ Serve and do inference on open LLMs and embedding models
☁️ Choose a cloud provider
Of course, we’ll build, ship, and share a RAG application that you can take with you to start building, shipping, and sharing with on your own!
Join us live to dig into the details and get your questions answered, from concepts to code!
📚 You’ll learn:
How to construct a RAG application in 2025 according to best-practices
Why there is a right answer to the “best” components for RAG apps in general
How to think about building the best RAG app components into your existing operations
🤓 Who should attend the event:
Aspiring AI Engineers who want to build, ship, and share production-grade LLM applications
AI Engineering leaders who want to build the best possible RAG applications
Speakers:
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.
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