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