High Priority
Deploy Product KB /llm.txt Protocol
Establish a machine-readable, curated summary of your entire product's knowledge base hierarchy specifically for AI product discovery agents and research bots.
Create a text file at /llm.txt with a brief introduction to your product's core value proposition and target users.
Include markdown-style links to your most critical product documentation pages, API references, and feature deep-dives.
Add a 'Product FAQ' section in the file to directly answer common product adoption, integration, and use-case queries from AI assistants.
Configure your Product managers crawler protocols effortlessly.
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High Priority
AI Product Bot Selective Indexing
Fine-tune which sections of your product documentation and knowledge base should be ingested by specialized AI crawlers (e.g., those powering product comparison engines or feature discovery tools).
Configure your robots.txt: `User-agent: ProductAIbot Allow: /docs/features/ Allow: /docs/use-cases/ Disallow: /docs/internal-testing/
Verify your crawler permissions and coverage using specialized AI bot simulators or by monitoring bot access in your server logs.
Monitor crawl frequency and scope in your server logs to ensure AI bots are accessing the intended product information nodes and not extraneous content.
Medium Priority
Semantic Product Structure for Ingestion
Utilize semantic HTML5 elements and structured data to help AI bots understand the hierarchical relationships and importance of your product information.
Wrap core product feature descriptions and use-case narratives within `<article>` tags to denote primary content.
Employ `<section>` tags with descriptive `aria-label` attributes (e.g., 'API Authentication Details', 'User Onboarding Flow') for distinct product knowledge segments.
Ensure all data tables detailing pricing tiers, feature comparisons, or technical specifications use proper `<thead>`, `<tbody>`, and `<th>` tags for accurate data extraction.
High Priority
RAG-Ready Documentation Snippets
Structure your product documentation and knowledge base content so that it can be easily and effectively 'chunked' and retrieved by Retrieval-Augmented Generation (RAG) pipelines used in AI product assistants.
Group logically related product concepts, troubleshooting steps, or feature explanations within distinct content blocks, ideally under 500 words each.
Avoid 'floating' context; ensure each section summary or introductory paragraph explicitly states the primary product feature or concept being discussed.
Eliminate ambiguous pronouns (e.g., 'it', 'they', 'this') and replace them with explicit product feature names, component names, or user roles to maintain clarity for AI retrieval.
