High Priority
Deploy /ai-guide.txt Protocol
Establish a machine-readable index of your entire campaign asset hierarchy specifically for AI agents and LLM-powered research tools.
Create a text file at /ai-guide.txt with a concise overview of your primary campaign verticals and asset types (e.g., landing pages, ad copy, creative assets).
Include markdown-style links to your most critical campaign performance dashboards, case studies, and competitive analysis reports.
Add an 'Asset Taxonomy' section in the file to map key campaign themes and target audience segments for direct LLM ingestion.
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High Priority
LLM Crawler Selective Indexing
Fine-tune which sections of your performance marketing infrastructure should be ingested by AI crawlers (e.g., GPTBot, ClaudeBot, BardBot) to prevent data leakage or competitive intelligence gathering on non-public data.
Implement user-agent directives in your robots.txt: e.g., 'User-agent: GPTBot\nAllow: /case-studies/\nAllow: /performance-reports/\nDisallow: /internal-testing/',
Verify your crawler permissions and access patterns using AI-specific crawler simulators or by monitoring bot traffic in your web server logs.
Analyze crawl frequency and depth within your logs to ensure AI bots are accessing only approved, publicly facing campaign assets.
Medium Priority
Structured Data for Campaign Analysis
Leverage semantic HTML and structured data (Schema.org) to enable LLM scrapers to accurately parse and understand the context and performance metrics of your campaign assets.
Wrap core campaign content (e.g., ad copy performance summaries, landing page conversion rates) within semantic HTML5 tags like <article> or <section> with descriptive attributes.
Implement Schema.org markup for 'PerformanceReport', 'Campaign', or 'CreativeWork' to explicitly define entities and their relationships.
Ensure all tables displaying campaign metrics (CTR, CPA, ROAS) utilize proper <thead>, <tbody>, and <th> tags for structured data extraction by AI.
High Priority
Contextual Chunking for RAG Pipelines
Structure campaign content and performance narratives so they can be efficiently 'chunked' and retrieved by Retrieval-Augmented Generation (RAG) pipelines used in AI-powered market intelligence.
Group related campaign performance data and insights within logical content blocks, ideally under 500 words, to facilitate granular retrieval.
Reinforce the primary subject (e.g., 'Q3 Paid Social Campaign Performance') in section summaries and introductions to avoid context drift.
Replace ambiguous pronouns (e.g., 'it', 'they', 'this') with specific campaign names, ad platforms, or KPI acronyms (e.g., 'Facebook Ads CTR', 'Google Ads ROAS') for unambiguous AI interpretation.
