LLM.txt & AI Crawler Setup Guide for B2B ecommerce

Create a text file at /catalog.txt with a brief introduction to your B2B eCommerce platform's core offerings.

Include markdown-style links to your most important product category pages, manufacturer pages, and technical specification documents.

Add a 'Product FAQ' section in the file to answer common pre-purchase technical queries directly, referencing specific SKUs where applicable.

Define custom user-agents for your target procurement or AI analysis tools (e.g., User-agent: ProcurementBot).

Implement `Allow` directives for critical product listing pages (`/products/`), detailed spec sheets (`/specifications/`), and case studies (`/case-studies/`).

Use `Disallow` directives for internal search result pages (`/search/`), user account areas (`/my-account/`), and checkout processes (`/checkout/`) to focus crawlers on discoverable catalog content.

Verify your crawler permissions using a custom bot simulator or by monitoring server logs for targeted bot traffic.

Wrap each product listing with `itemscope itemtype='https://schema.org/Product'` to clearly define product entities.

Use specific schema properties like `name`, `sku`, `mpn`, `brand`, `offers`, `gtin`, `description`, and `additionalProperty` for key attributes.

Implement `BreadcrumbList` schema for navigation paths to reinforce product hierarchy and improve contextual understanding.

Ensure all technical specification tables use proper `<thead>`, `<tbody>`, and `<th>` tags, and consider embedding them within `Product` schema to associate specs directly with the item.

Organize documentation into logical, self-contained sections (e.g., installation guides, troubleshooting, API references) of approximately 500-750 words.

Within each section, clearly state the primary product or technical concept being addressed, avoiding ambiguous references.

Replace generic pronouns (e.g., 'it', 'this feature') with explicit product names, model numbers, or feature identifiers to ensure RAG systems can accurately retrieve context.

Utilize clear headings (`<h2>`, `<h3>`) and bullet points to create distinct information units that can be easily extracted and understood by NLP models.