Most common query rewrite patterns | Trakkr Research
Research tracker page for the study How AI Translates Your Questions, focusing on the most common query rewrite patterns.
Methodology: Built from 11,521 captured prompt-to-query pairs observed in OpenAI web search calls, with 100% search-query coverage in the sampled dataset.
Summary
Benchmark data indicates that AI query rewrites are dominated by heavy replacement, list-style framing, business-term insertion, and freshness signals. The model rarely preserves the original prompt, instead transforming vague inputs into evaluative, time-bound, or geographically specific retrieval searches.
Benchmark rows
| Metric | Value | Context |
|---|---|---|
| Exact match rate | 0.17% | Only 20 of 11,521 pairs matched exactly. |
| Complete rewrites | 31.85% | 3,670 pairs fell into the complete rewrite bucket. |
| Year injection rate | 25.66% | 2,956 queries injected a year term. |
| Brand insertion rate | 15.24% | 1,756 queries inserted brand names not in the prompt. |
Ranked view
| Item | Value | Detail |
|---|---|---|
| Best-of framing | 20.21% | The model frequently turns vague prompts into evaluative searches. |
| List framing | 20.13% | List language is one of the most common retrieval transformations. |
| Year injection | 25.66% | Freshness is a recurring rewrite habit. |
| Brand insertion | 15.24% | The model often jumps straight to likely market leaders. |
| Location addition | 13.19% | Queries are often narrowed geographically before retrieval. |
Related pages
Continue through the same study cluster.
- does ai search your exact words - Related answer page
- how aggressive are ai query rewrites - Related answer page
- the exact match rate is only zero point one seven percent - Related fact page
Data & Sources
- How AI Translates Your Questions - Flagship study behind this page
- Page JSON - Machine-readable companion file