What These Controls Actually Do
An LLM generates text one token at a time. At each step, the model produces a probability distribution across its entire vocabulary — every possible next word, subword, or punctuation mark, each assigned a likelihood score. The model doesn't just pick the most likely token every time. It samples from that distribution, which is why the same prompt produces different outputs on different runs.
Temperature is the parameter that shapes that distribution before sampling occurs. Technically, it scales the raw scores (logits) that feed into the probability calculation. At temperature=1, the distribution is unchanged from what the model produces natively. Lower values sharpen the distribution — the highest-probability tokens become relatively more dominant, low-probability tokens become relatively less likely to be selected. At temperature=0, the model stops sampling entirely and always picks the single most probable token. Higher values flatten the distribution, making a wider range of tokens plausible and outputs more varied, sometimes more surprising, occasionally incoherent. Most providers set a default somewhere between 0.7 and 1.0, though defaults vary and are subject to change.
Top-p sampling (also called nucleus sampling) works differently — it truncates the distribution rather than reshaping it. The model ranks all tokens by probability, then considers only the smallest set of tokens whose cumulative probability reaches the threshold p. At top-p=0.9, the model samples exclusively from whatever tokens together account for 90% of the probability mass. This prevents the model from ever selecting very low-probability tokens, regardless of what temperature is doing to the distribution. Temperature and top-p are typically used together; they're not alternatives.
Seed is a value passed to the random number generator that drives sampling. With identical inputs, identical parameters, and a fixed seed, the model produces the same output. This is the closest thing to determinism available in standard LLM inference — and it comes with a caveat worth knowing: even with seed set, some providers don't guarantee bit-for-bit reproducibility across infrastructure changes, because floating-point arithmetic on GPUs can introduce tiny variations at scale. Reproducibility with seed is reliable enough for most QA purposes. It is not a cryptographic guarantee.
Why This Becomes a Governance Conversation
Take a federal agency deploying an LLM to help analysts summarize regulatory comments or draft initial responses to constituent inquiries. The system works well in testing. Then an auditor asks a straightforward question: what did the system tell this analyst on this date about this submission?
If the system was running at temperature=0.8 with no seed logged, the honest answer is: something in this probability distribution, and we can't reproduce it exactly. That answer does not survive an audit.
Temperature and seed, at that point, are no longer model configuration details. They're governance parameters. Low temperature plus a logged seed gives you reproducible outputs — the same input, same settings, same result. Regression testing becomes possible: change a system prompt, rerun the same queries with the same seed, compare outputs systematically. Without that, you're evaluating a moving target.
The production tradeoff is real. Temperature=0 produces consistent, auditable outputs, but it also produces outputs that can feel mechanical and repetitive over long generations. Temperature=0.7 produces more natural, varied text, but makes QA harder and audit trails incomplete. The right setting depends on what the system is doing. A document classifier probably wants temperature near zero. A drafting assistant probably wants more latitude. The decision is a policy decision, not a default to accept uncritically.
Enterprise buyers who've thought carefully about this will ask: what temperature are you running in production, how are you logging it, and how are you testing for output drift when your prompts change? If you haven't heard those questions yet, you will.
Okta Concept Mapping
The closest IDAM analogue to seed-plus-low-temperature is policy evaluation consistency: given the same request and the same context, an authorization engine produces the same decision. Every time. That determinism is structural in IDAM — it's not a parameter you configure, it's an assumption baked into how policy engines work.
The analogy breaks at a specific point, and the break matters: in an LLM, variability is a design choice, not a failure mode. A policy engine that sometimes granted access and sometimes denied it for identical requests would be a critical defect. An LLM running at temperature=0.8 is working exactly as designed. Enterprise buyers trained on IDAM intuition will instinctively treat output variability as something to be fixed. The more accurate framing is that it's a parameter to be governed — and governance means logging it, setting it deliberately, and testing against it.
When a CISO or agency CIO asks about auditability of AI-generated outputs, temperature and seed are the mechanism behind the answer. Reproducible outputs require low temperature and logged seeds. Evaluation frameworks that benchmark model behavior run at temperature=0 for exactly this reason — controlled comparison requires a controlled baseline, regardless of what temperature the system runs in production. That distinction is what separates a fluent answer from a vague one.