AI systems don’t get better by throwing more tokens at them. They get better when you engineer the right context. This session dives deep into the practical strategies behind min‑maxing AI token consumption while increasing output quality. We’ll go beyond traditional prompt engineering and explore reusable context blocks, hierarchical prompting, structured memory, and domain‑specific scaffolding that consistently produce higher‑quality results with fewer tokens.

Attendees will learn how to build modular context architectures, design reusable “prompt primitives,” and apply compression techniques that preserve meaning while reducing cost. Whether you’re building agents, workflows, or high‑volume LLM applications, this talk gives you the tools to scale intelligently, without sacrificing quality.