AI systems need more than intelligence; they need context. Without it, even the most advanced models can misinterpret information, lose track of details, or arrive at conclusions that don’t hold up. Context engineering is emerging as a discipline that shapes how AI perceives, recalls, and reasons about information.
This talk will explore how context provides the foundation for reasoning, problem solving, and explainability in AI. We will look at techniques such as connected memory, contextual retrieval, and graph-based knowledge representation that give large language models a more reliable way to connect information and draw logical conclusions.
Attendees will come away with a practical understanding of how to design effective context pipelines that align AI with real-world knowledge and user intent, and why context engineering is becoming a central part of building trustworthy and impactful AI systems.