Researchers published the results of a study showing how AI search rankings can be systematically influenced, with a high success rate for product search tests that also generalizes to other categories like travel.
The name of the research paper is Controlling Output Rankings in Generative Engines for LLM-based Search and the approach to optimization is called CORE, a way to influence output rankings in LLMs.
Caveat About The CORE Research
The testing and the reported results were done with actual LLMs queried via an API.
They tested:
- Claude 4
- Gemini 2.5
- GPT-4o
- Grok-3
They did not test AI Overviews, ChatGPT or Claude through their consumer interfaces. The importance of this distinction is that the normal kinds of personalization will not play a role. Also, the testing was limited to just the candidate search results.
Also, when the researchers queried the target LLMs (Claude-4, Gemini-2.5, GPT-4o, and Grok-3) via an API, the models did not rely on RAG or their own external search tools. Instead, the researchers manually supplied the “retrieved” data as part of the input prompt.
Why The Research Matters
CORE is a proof-of-concept for strategically optimizing text with reasoning and reviews. It also shows that LLMs respond differently to reviews and reasoning-based changes to text.
Reverse Engineering A Black Box
Understanding exactly what to do to improve AI search engine rankings is a classic black box problem. A black box problem is where you can see what goes into a box (the input) and what comes out (the output), but what happens inside the box is unknown.
The researchers in this study employed two strategies for reverse engineering generative AI to identify what optimizations were best for influencing rankings.
They used two reverse-engineering approaches:
- Query-Based Solution
- Shadow Model Solution
Of the two approaches, the Query-Based Solution performed better than the Shadow Model approach.
The percentages of top ranked optimizations of bottom ranked pages:
- Query-based Top-1 ≈ 77–82%
- Shadow model Top-1 ≈ 30–34%
Query-Based Solution
The query-based solution operates under the constraint that the researchers cannot access model internals, so they treat the LLM as a black box.
They repeatedly modify the document text. After each modification, they resubmit the candidate list to the LLM and observe the new ranking. The modify and test loop continues until a target ranking criterion or iteration limit is reached.
The query-based solution uses an LLM to add text to the target document. This is content expansion, not content editing.
They used two kinds of content expansion:
- Reasoning-Based Generation
Adds explanatory language describing why the item satisfies the query. - Review-Based Generation.
Adds evaluative content, review-like language about the item.
These are not random edits. They are changes tested as separate strategies, which the researchers then evaluate the rankings to determine whether or not the change had a…
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