TF-IDF tools vs embedding similarity for content coverage: measuring different things

TF-IDF tools vs embedding similarity for content coverage: measuring different things

Question: when scoring whether your draft "covers the topic," do term-frequency tools (TF-IDF style) or embedding-based similarity give better guidance?

Evidence: TF-IDF tools (Surfer/Clearscope-style term lists) tell you which words top pages share — useful, but they reward lexical matching and can be gamed by stuffing terms. Embedding similarity (comparing the meaning vector of your text to top results) captures semantic coverage and resists keyword stuffing, but gives vaguer guidance — a number, not a checklist. On one rewrite, hitting the TF-IDF term targets raised the embedding similarity only marginally; the gain came from answering missing sub-questions, which neither tool names directly.

Nuance: TF-IDF tools are an actionable proxy; embeddings are a truer-but-blunter measure. Use term tools to find obvious omissions, then judge information gain by hand.

Method note: one rewrite scored with both a term-coverage tool and cosine similarity to top results.

Caveat: n=1 document; both are proxies for relevance, not relevance itself.

Confidence: low