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Context Engineering for AI Agents

Nyah Macklin, Senior Developer Advocate for AI at Neo4j, will show you how to engineer context effectively for AI applications.
Nyah Macklin
Nyah Macklin

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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.

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.

Session Resources + Slides

Topics

AI AgentsArtificial IntelligenceLarge Language Models

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