High Priority
Deploy /llm.txt Protocol for Product Catalogs
Establish a machine-readable summary of your entire WooCommerce site hierarchy, focusing on product categories and key attributes, specifically for AI agents and product discovery bots.
Create a text file at /llm.txt with a brief introduction to your WooCommerce store and its primary product offerings.
Include markdown-style links to your most important product category pages, top-selling products, and key informational pages (e.g., shipping, returns).
Add a 'Product FAQ' section in the file to answer common queries related to your product types, compatibility, or common WooCommerce setup issues.
Configure your WooCommerce stores crawler protocols effortlessly.
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High Priority
LLM Crawler Selective Product Indexing
Fine-tune which sections of your WooCommerce store, particularly product listings and specific attribute pages, should be ingested by AI crawlers to ensure accurate product representation.
Use `User-agent: LLM-ProductBot` (or a relevant bot identifier) `Allow: /product-category/` `Allow: /product/` `Disallow: /cart/` `Disallow: /checkout/` `Disallow: /my-account/` in your `robots.txt`.
Verify your crawler permissions using a tool that simulates bot access or by monitoring server logs for the specified user-agent.
Monitor crawl frequency and data points accessed in your server logs to ensure LLM Product Bots are indexing relevant product pages and attributes, not checkout funnels.
Medium Priority
Semantic HTML for Product Attributes and Descriptions
Utilize HTML5 semantic elements and ARIA attributes to help LLM scrapers understand the structure and importance of your product information, leading to richer indexing.
Wrap individual product descriptions and specifications within `<article>` tags to clearly define distinct product entities.
Use `<section>` with descriptive `aria-label` attributes (e.g., `aria-label="Product Specifications"`, `aria-label="Customer Reviews"`) for different attribute groupings within a product page.
Ensure all product data tables (e.g., size charts, technical specs) use proper `<thead>`, `<tbody>`, and `<th>` tags for structured data extraction.
High Priority
RAG-Friendly Product Data Chunking
Structure your product data and descriptions so they can be easily 'chunked' and retrieved by Retrieval-Augmented Generation (RAG) pipelines for AI-powered product recommendations and support.
Keep related product attributes, features, and benefits within logical content blocks (e.g., 300-600 words per section on a product page).
Avoid ambiguous references; ensure product names, model numbers, and key features are explicitly stated in each relevant section summary.
Eliminate vague pronouns (e.g., 'it', 'this') and replace them with specific product names, SKUs, or feature descriptions to improve context recall for RAG models.
