Cosine Similarity Tests Prove Multi-Topic Paragraphs Dilute AI Retrieval to a 0.5 Baseline

To reverse-engineer AI Overviews, I score paragraphs against target queries using distance measures like cosine similarity, reveals Mike King.

While the SEO industry often dismisses chunking as a scam, test data shows that a single paragraph covering two distinct topics dilutes the vector relevance score across both prompts, dropping to a baseline of roughly 0.5.

Splitting that text block in half forces an immediate mathematical shift.

The vector relevance score for the primary query increases, while the score for the secondary query drops.

Retrieval-augmented generation (RAG) systems demand atomic units of understanding.

When the pipeline extracts chunks to feed the language model, a wall of text blending multiple concepts destroys the concentrated relevance required for extraction.

Structural isolation guarantees survival in modern agentic RAG pipelines.

Configure pages with strict heading-to-section topical boundaries, ensuring each heading gates a text block dedicated exclusively to one idea.

Instead of blindly passing the top search results to the LLM, agentic systems review, score, and re-rank individual chunks at every stage.

Clean, single-topic blocks dominate this re-ranking phase, while diluted paragraphs fall out of the pipeline before generation.