# The answer block - the only content format with evidence behind it > One peer-reviewed test of nine content-rewriting methods. Quotations added 40.9%, keyword stuffing lost 8.3%. What the study actually measured. Published: 2026-02-28 Updated: 2026-02-28 Canonical: https://augustynglowacki.com/posts/the-answer-block-evidence/ ## What did the study actually measure? Not retrieval. The researchers built their own answer-generation harness, gave it a query plus five already-retrieved source documents, and measured how much of the word count in the generated answer each source's content won - a metric called Position-Adjusted Word Count. That's a critical distinction: the study measures how much of an answer a source wins once it's already one of five candidates in the room, not whether an optimization gets you into the room. The paper's authors state this limitation themselves: "owing to the black-box nature of search engine algorithms, we didn't evaluate how GEO methods affect search rankings." ## What were the actual effect sizes? Nine methods, tested against a no-optimization baseline of 19.3 points on the primary metric ([GEO, KDD 2024](https://arxiv.org/abs/2311.09735)). Quotation Addition scored 27.2, a **+40.9%** improvement - the best of all nine. Cite Sources (adding citations to reliable sources in the body text) scored 24.6, **+27.5%**. Statistics Addition scored 25.2, **+30.6%**. Keyword Stuffing scored 17.7, **-8.3%** - the only method that landed below the unoptimized baseline. The authors' own wording on that last one: "while widely used for Search Engine Optimization, we find such methods offer little to no improvement on generative engine's responses." > "While widely used for Search Engine Optimization, we find such methods offer little to no improvement on generative engine's responses." > > - Aggarwal et al., "GEO: Generative Engine Optimization," KDD 2024 That line is about keyword stuffing, the one method of nine that made answers worse, not better. ## Did the effect hold up on a real commercial engine? Partly. [The paper's](https://arxiv.org/abs/2311.09735) one test against a live product, Perplexity, showed the effect shrinking. Quotation Addition dropped from +40.9% to +20.7% on Perplexity; Statistics Addition dropped from +30.6% to +8.7% on the same word-count metric, though it rose to +37.2% on the paper's separate, LLM-judged "Subjective Impression" score. Keyword Stuffing still underperformed, at -9.1%. The direction held, but the shrink wasn't uniform: Quotation Addition's effect roughly halved, while Statistics Addition's effect shrank by about 70%. Anyone quoting the +40.9% figure as a 2026-engine guarantee is overstating what a 2023-era research harness on one commercial engine actually showed. ## Why does an answer engine reward quotes and statistics specifically? Because it can't paraphrase a number or a named quote the way it paraphrases an idea. "37%" and "$49 per month" travel into a generated answer intact, and they drag their source attribution with them. That's the mechanism, and it's the same reason a separate, independently vendor-run study found statistic lines and table rows carry the highest word-for-word extraction rates of any passage format measured - 61% and 58% respectively, against 12% for plain narrative prose ([MaxAEO, 3,200 cited passages, hand-classified, no data release - treat as directional](https://maxaeo.ai/blog/what-content-ai-quotes-most/)). Table rows and statistic lines get quoted word for word **58%** and **61%** of the time - versus **12%** for plain narrative prose ([MaxAEO, 3,200 cited passages, hand-classified, no data release - treat as directional](https://maxaeo.ai/blog/what-content-ai-quotes-most/)). ## What's the honest limitation on all of this? The primary engine in the anchor study is a research harness, not ChatGPT or Perplexity as they exist today - GPT-3.5-era models, one fixed prompt template, five sources, one 2023 snapshot. Half the paper's headline numbers come from a metric scored by GPT-3.5 rather than by humans. And the measurement conditions on already being retrieved; nothing here tells you whether adding quotes and statistics gets a page discovered in the first place. The direction is the most solid finding available in this field. The exact percentages are not a promise about any specific engine today. ## What does an answer block that follows this evidence actually look like? Before: "Server-rendering AI crawlers can't reach client-rendered content is a growing concern, and it happened faster than expected." After: "Median time to first citation was 6.81 days across roughly 900 newly published pages (Profound, May 2026)." The second version names a number, a subject, a source, and a period, in one sentence - the shape that survives being lifted out of the page and quoted with the link stripped. It is the rule I apply to every page on this site. ## Sources - [Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande, "GEO: Generative Engine Optimization," KDD 2024, arXiv 2311.09735](https://arxiv.org/abs/2311.09735) - [MaxAEO, "What Content AI Quotes Most"](https://maxaeo.ai/blog/what-content-ai-quotes-most/) ## Sources - [Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande, "GEO: Generative Engine Optimization," KDD 2024, arXiv 2311.09735](https://arxiv.org/abs/2311.09735) - [MaxAEO, "What Content AI Quotes Most"](https://maxaeo.ai/blog/what-content-ai-quotes-most/)