LLMs, Graph Databases and RAG in the Cloud
- Date
- 2024-04-18
- Location
- Austin, TX, USA
- Host
- Neo4j
About this event
Large language models are powerful on their own, but the real challenge starts when you need them to work with live context, connected data, and production cloud systems. This event is for people who want to understand how LLMs, graph databases, and retrieval-augmented generation fit together in practice, and what that combination makes possible when you build in the cloud. About the Event This is an in-person session in Austin focused on one of the most useful intersections in modern AI: combining LLMs with graph-based data and RAG workflows to produce more reliable, context-aware applications. Rather than treating these as separate trends, the event puts them in the same conversation so attendees can see how each piece supports the others. You can expect a technical, applied discussion grounded in real architecture questions. How should knowledge be structured for better retrieval? When does a graph database add value over more familiar storage patterns? What changes when your LAG and RAG pipelines need to run in the cloud with scale, latency, and operational concerns in mind? This event is designed to help answer those questions in a way that feels concrete rather than abstract. The format is built for people who want substance. Whether you are exploring your first production-grade AI system or refining an existing approach, the goal is to give you a clearer mental model of the stack, the tradeoffs, and the design decisions that matter most. What to Expect Expect a focused look at the relationship between three fast-moving areas: LLMs, graph databases, and cloud-based RAG. The event will likely move from core concepts into implementation patterns, helping attendees connect strategic ideas with technical execution. Topics may include: How LLM-powered applications benefit from retrieval instead of relying only on model memory Why graph databases are useful for representing relationships, dependencies, and connected knowledge How graph-structured data can improve retrieval quality and context assembly What cloud deployment changes about performance, orchestration, observability, and scale Common architectural patterns for building AI systems that need trustworthy answers and structured context You should also expect practical discussion around system design choices. That includes the kinds of questions teams ask when moving from prototypes to production: where the data lives, how retrieval is shaped, how context is passed into models, and how infrastructure decisions affect reliability and cost. Because this is in person, there is also value beyond the formal content. Events like this create room for direct conversations with other builders, engineers, architects, and technical leaders who are working through similar challenges and evaluating similar tooling decisions. Why Attend If you work anywhere near AI systems, search, knowledge platforms, data architecture, or cloud infrastructure, this topic matters now. Many teams already understand the promise of LLMs, but the harder question is how to make them useful in environments where correctness, context, and connected data matter. That is exactly where graph databases and RAG become highly relevant. Attending can help you sharpen your understanding of when these approaches are worth using and when they are not. You will leave better equipped to think through architecture choices instead of treating AI application design like a black box. This event is especially valuable if you want to: Understand the practical role of retrieval in modern AI applications See how connected data models can support better reasoning and richer context Learn how cloud considerations shape AI system design in the real world Compare approaches for building more accurate and explainable LLM-driven experiences Meet others who are actively building, evaluating, or scaling similar systems The biggest benefit is clarity. Instead of hearing about LLMs, graphs, and RAG as separate buzzwords, you will get a more integrated view of how they work together and where that combination can create real technical advantage. Practical Details The event takes place in person in Austin, USA on Thursday, April 18 at 4:00 PM CDT. If you prefer learning through live discussion and face-to-face conversation, this format makes it easier to ask questions, compare notes, and build connections with people working in adjacent areas. This is a strong fit for attendees coming from engineering, data, AI, and cloud backgrounds, but the topic is broad enough to be useful for technical decision-makers as well. You do not need to arrive with a finished point of view; curiosity and a working interest in applied AI systems are enough to get value from the session. To get the most out of it, come ready to think about your own use cases. Whether you are building internal knowledge assistants, search and discovery features, analytics tools, or domain-specific AI products, the ideas discussed here should map well to real project decisions. If you have been looking for a practical, cloud-relevant conversation about how LLMs interact with structured knowledge and retrieval systems, this event offers a timely reason to show up in person.
Who should attend
This is for you if you want a more practical understanding of how modern AI systems are actually put together, especially when LLMs need reliable context and real-world data behind them. - You are a **software engineer or ML engineer** exploring how to move from LLM demos to applications that can retrieve, reason over, and use external knowledge more effectively. - You are a **data engineer, data architect, or database practitioner** interested in how graph databases fit into AI workflows and where connected data can improve retrieval and context quality. - You work in **cloud, platform, or infrastructure roles** and want to understand the operational side of running RAG-based systems in cloud environments. - You are a **technical product manager, architect, or engineering leader** evaluating design choices for AI features and need a clearer view of the tradeoffs across models, data, and infrastructure. - You are building or planning **search, knowledge, assistant, or recommendation experiences** and want ideas that apply to real systems rather than generic AI talk. - You enjoy being in a room with other technically curious people in Austin who are actively thinking about AI, data, and cloud architecture.