LLM.txt & AI Crawler Setup Guide for Cafes

Create a text file at /cafe-crawl.txt with a brief introduction to your cafe's unique selling propositions (e.g., artisanal coffee, vegan pastries, community events).

Include markdown-style links to your most important pages: menu, location/hours, online ordering, customer testimonials, and blog posts about coffee sourcing or baking techniques.

Add a 'FAQ' section in the file to answer common queries from AI models about your operating hours, allergy information, reservation policies, or Wi-Fi availability.

User-agent: MenuBot Allow: /menu/ Allow: /specials/ Disallow: /admin/

User-agent: ReviewCrawler Allow: /reviews/ Allow: /testimonials/ Disallow: /internal-feedback/

Verify your crawler permissions using a tool like Google's Search Console or by simulating bot requests to ensure MenuBot and ReviewCrawler are hitting the correct pages.

Wrap your main menu sections (e.g., 'Espresso Drinks', 'Pastries', 'Sandwiches') in <section> tags with descriptive headings (e.g., <h2>Espresso Drinks</h2>).

Use <article> tags for individual menu items, including details like ingredients, price, and dietary information (e.g., 'Vegan', 'Gluten-Free').

Implement Schema.org markup for 'Cafe' or 'Restaurant' with properties for 'servesCuisine', 'hasMenu', 'address', 'openingHours', and 'acceptsReservations'.

Keep detailed descriptions of individual menu items, including key ingredients and potential allergens, within distinct content blocks (e.g., 300-700 words per item).

Ensure each menu item description clearly states its primary components and any common allergens (e.g., 'Contains nuts, dairy. Prepared in a facility that handles gluten.').

Avoid ambiguous terms; explicitly name ingredients (e.g., 'almond milk' instead of 'non-dairy option') and dietary features ('vegan' not just 'plant-based').