A metric that checks how often the correct or expected result for a query shows up in the top K items returned by an AI search system.
Professionals optimizing brand visibility and content performance within AI search systems.
01What it is and how it works
AI search systems do not always return every possible answer. They rank results and often cut the list short at a fixed number, called K. R@K asks: of all the answers that should have been found, how many actually landed in that top K list? The system compares the returned list against a set of known correct answers, usually built from prior queries or human judgments. Each correct answer found within the first K positions counts as a hit. The total hits are divided by the total number of correct answers that exist for the query set. The result is a percentage between 0 and 100. A high R@K means the brand or entity is being surfaced early and often. A low R@K means it is being buried or skipped entirely. This matters because most users never scroll past the first few results, especially in chat-style answers where only one or two sources are cited.
R@K counts how many of the right answers appear in the first K results. If 8 out of 10 expected answers show up in the top 10 results, R@K is 80%.
02What to do about it
Start by listing the queries where your brand should reasonably appear. Run those queries in the AI search tool you care about and record which results come back in the top 5 or top 10. If your brand is missing, check whether your content matches the query intent closely enough. Update product pages, FAQs, or help articles so they directly answer those questions. Add clear entity markers like brand name, product name, and category so the model can match them. Submit updated pages for indexing if the platform supports it. Repeat weekly and track changes in R@K. Focus first on queries with high traffic or high business value.
03How it is measured or noticed
You need two things: a list of queries and a list of expected correct answers for each query. The expected answers usually come from your own knowledge base, past search logs, or human raters. Run each query through the AI search system and capture the top K results. Count how many of the expected answers appear in that top K list. Divide that count by the total number of expected answers. That gives you R@K for one query. Average across all queries to get an overall score. You can track this in a spreadsheet or in a dashboard that pulls results from the search API.
04Common mistakes
- Treating R@K as a measure of brand awareness instead of answer visibility
- Using too small a K value, like K=1, and ignoring that users may see more results
- Comparing R@K across different AI search tools without normalizing the query sets
- Failing to update the list of expected answers when product lines or services change
- Measuring only high-volume queries and missing long-tail opportunities where R@K is low
05Limits
R@K only applies when there is a known set of correct answers. It does not work for exploratory queries where any relevant result is acceptable. It is also not a measure of result quality or user satisfaction. A brand can have high R@K but still appear in a misleading or low-quality context. R@K is often confused with precision at K, which measures how many of the returned results are actually correct, not how many correct results were returned. The two metrics answer different questions and should not be used interchangeably.
06Worked example
A retail brand tracks 10 queries where customers ask about their products. For each query, the brand expects to appear in the top 10 AI search results. After running the queries, the brand finds it appeared in the top 10 for 7 of the 10 queries. R@K at 10 is 70%. The brand then updates its product descriptions and re-runs the test. This time, it appears for 9 of the 10 queries, raising R@K to 90%.
Frequently asked questions
What is Recall at K (R@K) and why is it important for AI search?
Recall at K (R@K) measures how often a relevant result appears in the top K results returned by an AI search system. It indicates how well your brand or content is captured within a limited set of results, which is crucial for understanding visibility in search rankings.
How is Recall at K (R@K) different from Precision at K?
While R@K measures whether relevant results appear in the top K, Precision at K measures how many of the top K results are actually relevant. R@K focuses on coverage of relevant content, whereas Precision focuses on accuracy of returned results.
What do I need to measure Recall at K (R@K) for my brand?
You need a list of queries where your brand should appear and a list of expected correct answers for each query. These are used to evaluate whether your content appears in the top K results returned by the AI search system.
Can Recall at K (R@K) be used for all types of search queries?
No, R@K only applies when there is a known set of correct answers. It works best for queries where relevance is well-defined and can be objectively measured against expected results.
What happens if my brand doesn’t appear in the top K results?
If your brand doesn’t appear in the top K results, your Recall at K (R@K) score drops, indicating reduced visibility. This can lead to missed opportunities for user engagement and brand recognition.
How do I improve my Recall at K (R@K) score?
Start by identifying queries where your brand should appear and ensure your content is optimized for those queries. Improving relevance and visibility in search results will help increase your R@K score.
Is Recall at K (R@K) affected by the number of results returned?
Yes, R@K is directly affected by the number of results returned (K). A smaller K makes it harder to achieve high recall, while a larger K increases the chance of including relevant results.
Asked out loud
spoken, not typedThe same term in the words somebody uses speaking to an assistant rather than typing into a box — written from the situation, which is why each one carries the situation it came from.
Yes, if your brand appears in the top K results for relevant queries, your Recall at K (R@K) score is high. This means your content is being captured effectively by the AI search system.
Recall measures how many relevant results are found in the top K, while precision measures how many of the top K results are actually relevant. Both are important, but they answer different questions about search performance.
You can measure it using Recall at K (R@K). It tells you how often your content appears in the top K results for queries where it should be relevant. If it’s not showing up, your visibility is low.