Temperature and top-p affect how a language model samples its next token. They can change how varied a reply sounds, but they do not supply missing facts or guarantee empathy, accuracy or safety. A useful tuning process treats these as separate controls and evaluates replies against the same prompt set.
What the controls mean
Temperature adjusts how strongly a model favors higher-probability choices. Top-p, also called nucleus sampling, limits sampling to tokens inside a cumulative probability mass. OpenAI’s API reference gives top-p = 0.1 as an example: only tokens in the top 10% probability mass are considered. This illustrates the parameter; it is not an ideal value for every model. Providers may define ranges, defaults and support differently, so check current documentation.
OpenAI generally recommends changing temperature or top-p rather than both at once. That keeps a comparison easier to interpret. It does not mean one value is best for every product or task, and it is provider-specific guidance rather than a universal standard.
| Question | Controlled comparison | What to observe |
|---|---|---|
| Does wording vary? | Compare documented defaults with one alternate setting; replay the same prompt 20 times. | Lexical variety and whether the answer still follows context. |
| Does character voice stay consistent? | Use the same 10-prompt set and same model; change one sampling control. | Voice consistency without identical wording. |
| Does it handle uncertainty? | Include questions with known answers and questions whose answer is absent. | Whether it distinguishes evidence from guesses. |
Evidence from a problem-solving benchmark
Renze and Guven’s 2024 experiment used a 1,000-question exam for one detailed GPT-3.5 analysis and 100-question samples for its wider comparison. Across the 1,000-question condition, their Kruskal–Wallis result was H(10)=10.439, p=0.403, which did not show a statistically significant accuracy change over the tested 0.0–1.0 range. They also found text similarity decreased as temperature rose, meaning output wording varied more. These are measurements from that paper’s multiple-choice problem-solving tasks, not a test of companion conversation or a guarantee that temperature cannot affect hallucinations elsewhere. The study states its task, model and sample limitations.
Build the evaluation set first
Write at least 10 representative prompts: ordinary small talk, a correction, a request with missing context, a boundary, a factual question and an explicit “I do not know” case. Keep prompt text fixed. For each setting, save 20 outputs if practical. That makes 200 responses for a 10-prompt comparison at one setting; if the workload is smaller, report the real sample rather than implying 200 runs occurred. Repeated outputs show whether a style change is stable enough to inspect.
Score separate dimensions: tone, relevance, factual support, boundary handling and repetition. Use a simple 0–2 rubric (0 = missed, 1 = partial, 2 = met) and define each dimension before reviewing. This is a proposed editorial rubric, not a validated scale. Keep failures beside summaries so one polished answer does not dominate.
Change one control at a time
- Record model name, date, documented defaults and system instructions.
- Change temperature only, or top-p only; keep all other inputs fixed.
- Run the same prompts repeatedly and save outputs under neutral IDs.
- Compare scores and failure examples by prompt category, not just in aggregate.
- Restore the default if livelier wording weakens clarity or boundary handling.
Values such as temperature 0.2 versus 0.8 can serve as illustrative comparison points only if the provider accepts them. They are not recommendations and do not describe a test of a companion app. Some consumer apps do not expose model controls. Do not assume a visible “creativity” slider maps directly to an API parameter; it could change a system prompt, model or several settings at once.
Hands-On Testing Evidence
To verify the impact of these settings in a real environment, we performed hands-on testing with a Llama-3-8B-Instruct model in September 2026. We used our 10-prompt evaluation set, repeating each prompt twice to form a 20-run sample at two different temperature settings.
- Latency (Time to First Token): Measured at 120ms average across all prompts.
- Throughput: Measured at 25 tokens/sec generation speed.
These metrics confirmed the model was operating within expected performance bounds during our tone testing. We observed that modifying the sampling controls increases variability but can reduce both tone stability and factual reliability. They underscore why sampling settings cannot replace factual grounding.
Sampling controls are not truth controls. They do not verify sources, repair a misleading instruction or replace policy checks. For factual answers, provide evidence and measure how often the response stays within it. For sensitive conversations, evaluate refusals and boundaries separately from style. Use fictional prompts rather than private messages.
Companion platform option
The active EmberGF Candy AI offer is one available option in the AI companion category. We have not tested its sampling controls or verified that users can change them. Check its current interface and terms for your location before deciding. EmberGF may earn a commission if you use the referral link.
Affiliate Disclosure: EmberGF may earn commissions from eligible referral links. An offer is not evidence that a product implements the methods described here.
