A technique used by AI language models to select the next word or token from a probability distribution.
01What it is and how it works
Language models assign a probability to every possible next token (word or subword) based on the preceding context. Sampling selects the next token from that probability distribution rather than always taking the highest-probability token (greedy decoding). Two key parameters control sampling: temperature and top-p. Temperature scales the logits before applying softmax: a higher temperature (e.g., 1.0) flattens the distribution, making low-probability tokens more likely; a temperature of 0 forces greedy selection. Top-p (nucleus sampling) considers only the smallest set of tokens whose cumulative probability exceeds a threshold p (e.g., 0.9), cutting off the long tail. For brand measurement, sampling means the same query can yield different brand mentions across runs because the model may choose different tokens each time.
When an AI model writes a response, it doesn't always pick the most obvious word. Instead, it sometimes picks a less likely word to sound more natural. This randomness is called sampling, and it can make your brand appear more or less often depending on the settings.
02What to do about it
To account for sampling variability, run the same query multiple times (10–20 repetitions) and record the frequency of each brand mention. This gives a distribution rather than a single data point. When establishing a baseline, set temperature to 0 for deterministic output. If the AI search tool exposes sampling parameters, monitor them and note any changes. Optimize your content to increase its probability in the model's distribution: improve relevance, authority, and contextual fit. Use brand names in natural, well-supported contexts so they remain in the top-p set even when sampling is active.
03How it is measured or noticed
Measure variability by comparing brand mention frequency across repeated queries. For example, if a brand appears in 7 out of 10 runs with temperature 0.7 but in 10 out of 10 with temperature 0, sampling is affecting visibility. Tools that log the model's sampling parameters (temperature, top-p) allow you to correlate changes in brand presence with those settings. You can also notice sampling by observing that the same prompt produces different brand lists or rankings on successive calls.
04Common mistakes
- Assuming a single query result is representative of all AI responses.
- Ignoring sampling parameters when comparing brand visibility over time.
- Optimizing for the most likely token only, missing opportunities that sampling creates for less dominant brands.
- Not accounting for sampling variability in A/B tests or performance benchmarks.
- Confusing sampling with model hallucination or bias in training data.
05Limits
Sampling does not apply when temperature is set to 0 (greedy decoding) or when the model uses deterministic decoding. It is often confused with model hallucination, but sampling controls output variability, not factual accuracy. Sampling affects which tokens are chosen, but it does not change the underlying knowledge or probabilities the model learned. Other factors like prompt phrasing, context length, and model version also influence brand mentions and can interact with sampling.
06Worked example
A marketer asks an AI model 'What are the best CRM platforms?' with temperature=0.7. In 10 runs, Salesforce appears 8 times, HubSpot 6 times, Zoho 2 times. With temperature=0, Salesforce appears 10 times, HubSpot 0, Zoho 0. The sampling introduces variability that can benefit smaller brands if they are in the probability distribution.
Frequently asked questions
How is sampling different from temperature in AI language models?
Temperature controls the shape of the probability distribution that sampling draws from. Sampling is the actual process of picking a token from that distribution. A higher temperature makes the distribution flatter, increasing randomness, while a lower temperature makes it sharper, reducing variability.
Should I always run multiple queries to account for sampling variability?
Yes, if you need reliable brand mention frequency data. Sampling introduces randomness, so a single query might not reflect the true probability. Running the same query 10–20 times and averaging the results gives a more accurate picture.
How many repetitions do I need to get reliable brand mention data?
Typically 10–20 repetitions are enough for most use cases. If a brand is mentioned rarely, you may need more repetitions to detect it reliably. The exact number depends on the variability you observe in initial runs.
Does sampling still apply if the model uses greedy decoding?
No, sampling does not apply when temperature is set to 0 (greedy decoding) or when the model uses deterministic decoding. In those cases, the model always picks the most likely token, so there is no variability in output.
What breaks if I don't account for sampling variability?
You risk drawing incorrect conclusions about which brands are mentioned and how often. A brand might appear or disappear by chance, leading to misleading reports or decisions. You would notice inconsistent results when repeating the same query.
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, that's normal due to sampling variability in the AI model. To get reliable data, run the query at least 10 times and record how often each brand appears. That will give you a stable frequency.
Run the same query 10 to 20 times and count how many times each brand appears. The most frequent brand across those runs is your best estimate. One query is not enough because sampling makes each answer vary.
No, one query is unreliable because of sampling randomness. Run it at least 10 times to see if that brand consistently appears. If it only showed up once, it might be a fluke.