Most LLM failures are not caused by a bad prompt alone. They often emerge from missing, unstable, or poorly structured context: unclear goals, conflicting source material, weak evidence hierarchy, fragmented memory, vague role definitions, and undefined decision boundaries.
This talk introduces Context Architecture as the missing design layer between prompt engineering, retrieval, evals, governance, and production behavior. We’ll look at how context can be structured as a system: what the model needs to know, what it should prioritize, what it should ignore, how state should be carried forward, and where human judgment needs to remain explicit.
The session will share a practical framework for designing context packages, behavior constraints, signal flows, and handoff artifacts that make LLM systems more reliable, inspectable, and easier to improve over time. Rather than treating prompts as isolated instructions, the talk reframes context as infrastructure: the layer that shapes how an LLM interprets tasks, reasons through ambiguity, and behaves inside real workflows.
Chrys Li is an AI systems and service design strategist focused on designing intelligent systems inside complex organizations. Her work spans enterprise platforms, regulated environments, AI-enabled products, and large-scale service ecosystems, with a focus on translating ambiguity into structured, build-ready systems.
She has led systems and experience design initiatives across logistics, healthcare, financial services, telecom, education, enterprise software, and other sectors. More recently, her work has focused on AI behavior, context architecture, human-AI collaboration, and the design of operational frameworks that help teams move from AI experimentation to practical implementation.
Chrys is an independent consultant where she develops frameworks, workshops, and applied AI methods for product, design, and business teams adapting to AI-era systems.
