An experimental guide to Answer Engine Optimization (mapledeploy.ca) AI

The article argues that “answer engines” are increasingly shaping web discovery without traditional click-based search results, and it proposes an experimental Answer Engine Optimization approach. It recommends rewriting marketing content into markdown, publishing an /llms.txt index (and full /llms-full.txt), and serving raw markdown (with canonical link headers) to AI agents via content negotiation or a .md URL. It also suggests enriching markdown with metadata in YAML frontmatter so AI systems can better understand and cite the content.