AI systems require more than intelligence; they require context. Without it, even advanced models can misinterpret data, lose important details, or reach conclusions that do not hold up. Context engineering is an emerging discipline focused on how AI perceives, recalls, and reasons about information.

This session explores how context forms the foundation for reasoning, problem solving, and explainability in AI. It covers techniques such as connected memory, contextual retrieval, and graph-based knowledge representation that help large language models connect information and draw logical conclusions more reliably. Attendees will learn how to design effective context pipelines that align AI systems with real-world knowledge and user intent, and why context engineering is becoming central to building trustworthy and impactful AI applications.

What You Will Learn

• How context shapes reasoning and reliability in AI systems

• Techniques such as contextual retrieval, connected memory, and graph-based representation

• Practical design strategies for context pipelines that align AI with user intent

Who Should Attend

AI engineers, data scientists, software architects, and developers designing intelligent systems that rely on accurate, context-aware reasoning.