Context Engineering for AI Agents
Key Takeaways:- Learn how effective context management improves the performance of AI applications.
- Understand different context engineering techniques, from prompts to RAG and knowledge graphs.
- Discover how to use Neo4j as a scalable memory layer for AI chatbots and agents.
Description
As AI applications move beyond simple prompts, context has become one of the most important factors in performance, reliability, and user experience. From chatbots to autonomous agents, how you manage and retrieve context can make the difference between helpful systems and frustrating ones.
In this code-along webinar, Nyah Macklin, a Senior Developer Advocate for AI at Neo4j, will show you how to engineer context effectively for AI applications. You’ll explore multiple context management approaches—including prompt management, retrieval-augmented generation (RAG), and knowledge graphs—and learn how to use Neo4j as a memory layer for AI chatbots and agents. By the end, you’ll have practical patterns you can apply directly to your own AI systems.
Session Pre-Requisites
Ahead of time please create:
1. Github Account (please create ahead of time if you don't already have one). This sometimes includes installing 2-factor authentication which I believe is now mandatory for github accounts. Doing this ahead of time is imperative.
2. GraphAcademy Account (Click Login/Signup)
We will do most development inside Github Codespaces (a sandboxed environment) so you will not need a separate IDE.
Presenter Bio
Nyah is a software engineer and developer relations expert. They help developers use Neo4j for AI engineering. Nyah is also CTO and Founder of the Afros in AI technical community. Previously, Nyah was Lead Developer Advocate at Couchbase, and Chief Of Staff for the Commonwealth of Massachusetts.