How AI systems work
TL;DR: Authority Transfer Vector (ATV) is a high-authority third-party source that cites your domain, lending its trust to you in...
TL;DR: Chunk Extractability (0-100) measures how easily AI systems can pull self-contained content pieces from your pages. Pages with >70%...
TL;DR: Citation Safety measures how “safe” it feels for an AI to cite your page. Over-claiming lowers citation likelihood. Neutral...
TL;DR: Evidence Density measures the concentration of verifiable claims per content section. AIs prefer citing “claims with receipts.” Raise Evidence...
TL;DR: Extraction Noise Ratio is how much of what a bot extracts is template noise instead of main content. High...
Information Gain is the measure of new, non-redundant knowledge a content chunk provides.
TL;DR: llms.txt is an emerging standard for providing AI-specific guidance to large language models—think of it as robots.txt for LLMs....
TL;DR: Multimodal RAG Signals are optimizations that allow image/video content to be “read” by AI models (GPT-4o, Gemini). Flat images...
TL;DR: Time lag between publishing content and AI-generated answer appearance. RAG systems: 24–48 hours. Training-based (ChatGPT): 6+ weeks. FAII benchmark:...
TL;DR: Token Budget Efficiency measures information density per token processed. RAG systems have limited context windows. Bloated content gets truncated;...
