Erik Hanchett

This talk addresses the significant token costs associated with using AI agents, particularly large language models, and provides practical strategies for reducing these expenses. The core thesis is that by implementing specific techniques, developers can optimize agent performance and cost-efficiency without sacrificing functionality.

This talk introduces spec-driven development as a structured approach to software engineering, emphasizing the creation of detailed specifications and design documents before writing any code. This methodology is particularly effective when working with AI coding assistants, guiding them to produce higher-quality, more accurate code. The core idea is to leverage AI as an intern that requires clear direction, with spec-driven development providing that essential guidance.