It happens when a system assumes early search results are relevant, adds terms from those documents to the query, and unintentionally moves the meaning toward a different topic or subtopic.

It’s usually bad. It may increase the number of results, but those results become less precise because the system is now retrieving content about a different, unintended topic.

Mostly in pseudo-relevance feedback systems, query expansion algorithms, embedding-based retrieval models, and generative search tools using reformulations.

Because when content or search queries drift away from true intent, visibility, accuracy, and retrieval quality all decrease—impacting rankings, AI summaries, and LLM-based answers.

Query Drift analyzes Google Search Console data to detect when search queries hitting a page no longer match that page’s intended topic or purpose—revealing misalignment and new content opportunities.

It uses semantic similarity models (cosine similarity + LLM validation) to compare a page’s core topic against the queries it ranks for. Big gaps = drift.

Far far away, behind the word mountains, far from the countries Vokalia and Consonantia, there live the blind texts. Separated they live in Bookmarksgrove right at the coast

Traditional tools show what you rank for; Query Drift shows what you should rank for and where Google is confused. It’s alignment-first, not keyword-first.

You get clear corrective actions:

• Tighten the page intent
• Split content into new pages
• Add missing sections
• Improve semantic structure
• Build supporting content clusters

It essentially tells you how to fix the misalignment to regain relevance and visibility.