In the evolving landscape of digital content, the emergence of large language models (LLMs) is reshaping traditional SEO practices, leading to the development of new strategies like the llms.txt specification to enhance content accessibility for LLM-powered search tools such as ChatGPT and Gemini. Unlike traditional SEO methods that focus on structured metadata for deterministic systems, llms.txt allows for a more flexible, Markdown-based representation of site structures and content, enabling language models to better comprehend and navigate complex web pages. Knock, a notification infrastructure platform, has implemented this specification by creating a pipeline that generates llms.txt files from Markdown source files, despite challenges posed by interactive elements and context window limitations in current models. This shift towards LLM-based search visibility is particularly relevant for technology providers and developer tools, emphasizing the need for separate content optimization strategies for human readers and machine comprehension. As the industry continues to explore this uncharted territory, the importance of tools like llms.txt is expected to grow, offering opportunities to enhance how language models understand and interact with digital content.