Pular para o conteúdo principal

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

Crie sua conta gratuita

Pratique e programa seu progresso.

Continuar com o GoogleAo continuar, você aceita nossa Termos de Uso, nossos Política de Privacidade e que seus dados são armazenados nos EUA.

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

Temas

AI AgentsArtificial IntelligenceLarge Language Models

Relacionado

blog

Enhancing Large Language Models with Knowledge Graphs

Ver mais →