⚖️ AI Semantic Alignment
Why doesn’t the AI cite your content when answering specific queries?
📖 What does this tool measure?
When someone asks AI a question, the system converts that query into a vector and hunts for the closest semantic match among indexed content chunks. This tool uses the same embedding approach (BGE model, cosine similarity) that RAG pipelines rely on.
See Make AI Speak for You: The Definitive Guide to GEO, Ch. 2.3 & Ch. 3.5
❓ FAQ: GEO Impact
How is this different from keyword matching?
Keyword matching requires exact words. Semantic matching cares about meaning — different phrases can be close in meaning.
What score should I aim for?
0.8+ is a strong match. 0.6-0.8 is workable. Below 0.6 suggests your content drifts from what users are asking.
How do I close the gap?
Build a richer semantic field: use synonyms naturally, cover different angles, phrase FAQs the way real users ask.
模拟检索指令 (User Prompt)
设定用户向 AI 提问的具体情境
待测内容片段 (Candidate Segments)
提供您希望被引用的具体段落
诊断结论 (Diagnostic Report)
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平均召回概率
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低匹配噪声语料
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