High Priority
Deploy /affiliate.txt Protocol
Establish a machine-readable sitemap specifically for AI agents to understand your affiliate site's core structure and content offerings.
Create a text file at /affiliate.txt with a brief overview of your affiliate niche and primary product categories.
Include markdown-style links to your most critical review pages, comparison posts, and buyer's guides.
Add an 'Affiliate Program Info' section to directly address queries about commission structures or partner requirements, if applicable.
Configure your Affiliate sites crawler protocols effortlessly.
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High Priority
Custom Bot Selective Indexing
Fine-tune which sections of your affiliate site should be ingested by AI crawlers focused on product reviews and comparisons.
User-agent: AffiliateAIbot Allow: /reviews/ Allow: /best-products/ Disallow: /user-comments/
Verify your crawler permissions using a custom bot simulator or by checking server logs for targeted access.
Monitor crawl frequency in your server logs to ensure AI bots are prioritizing your key affiliate content and not ancillary pages.
Medium Priority
Semantic HTML for Product Data
Utilize HTML5 semantic tags and schema markup to help LLM crawlers accurately extract product specifications, pricing, and affiliate links.
Wrap individual product reviews within <article> tags to denote distinct content units.
Use <section> tags with descriptive 'aria-label' attributes for different product feature breakdowns or pros/cons lists.
Ensure all product comparison tables use proper `<thead>`, `<tbody>`, and `<th>` tags, and implement `Product` or `Offer` schema markup.
High Priority
RAG-Friendly Review Optimization
Structure your affiliate reviews and comparisons so they can be easily 'chunked' and utilized by Retrieval-Augmented Generation (RAG) pipelines for AI-generated summaries or Q&A.
Keep detailed product feature analyses within logical content blocks, ideally under 700 words.
Clearly state the product name and its primary benefit at the beginning of each review section; avoid ambiguous references.
Eliminate jargon or overly technical terms unless they are specific to the product category; define them clearly or use simpler synonyms.
