Term Distribution in Articles

Term Distribution in Articles refers to the strategic distribution of semantically relevant keywords throughout all content sections, rather than clustering them in opening paragraphs. RAG systems split articles into chunks (fragments of ~200–500 words) and vectorize each chunk separately — if all important terms cluster in the first 500 words, only the first chunk gets good embeddings while the rest becomes 'semantically empty,' making those sections unlikely to be retrieved by RAG systems. This also hurts performance in passage-based search results. Proper distribution ensures every H2 section contains relevant industry terms, synonyms, and meronyms, creating strong semantic signals across all fragments.

For example, an article about leasing with 6 H2 sections should distribute key terms like 'lease,' 'payment,' and 'VAT' throughout each section rather than front-loading them in the introduction. Each H2 section should contain enough industry terms for AI to categorize it correctly when evaluated independently. This approach directly impacts Passage Ranking — Google's algorithm that evaluates individual page fragments rather than entire pages, similar to how RAG systems process content chunks.