Why Does Your AI Chatbot Give a Different Answer to the Same Question Every Time?

Why does an LLM give a different answer to the exact same question?

Because an LLM doesn't look up a fixed answer, it predicts one token at a time from a probability distribution over its entire vocabulary, and then samples from that distribution rather than always taking the single most likely token. Generation is a chain of these probabilistic draws, dozens or hundreds of them per response, so two runs of the identical prompt can diverge at the very first token and compound from there into two structurally different answers.

A parameter called temperature controls how sharply peaked or flat that probability distribution is: low temperature concentrates the odds on the few most likely tokens, high temperature spreads probability across many more candidates, which is why creative-writing tools default to a higher setting and factual-lookup tools default lower.

Doesn't setting temperature to 0 make it fully deterministic?

No, and this trips up most teams the first time they hit it. Temperature 0 is supposed to force greedy decoding, always take the single most probable token, which should in theory be repeatable. In practice it isn't, and this isn't a vendor bug, it's a structural property of how inference actually runs. Anthropic's own Messages API reference states plainly that even with temperature set to 0.0, results will not be fully deterministic.

The reason is lower-level than the sampling parameter: floating-point arithmetic on a GPU is not strictly associative, so the exact order operations get batched and summed in can shift rounding by a tiny amount, and your request is very likely processed alongside other concurrent requests in a batch whose composition changes minute to minute. Thinking Machines Lab's technical breakdown of this exact problem traces it to how GPU kernels handle grouped, batched work, and for models built on a mixture-of-experts architecture there's a second layer on top of that, since which expert sub-network handles a token can itself shift with batch composition.

When does this non-determinism actually matter for a business?

It matters wherever an answer is being relied on as if it were a fact, not a first draft: compliance guidance, legal or policy language, a financial calculation, a customer-facing answer someone screenshots and holds the company to later. It matters less for brainstorming, first-pass copy, or anything a human is already going to review and edit before it goes anywhere.

The failure mode isn't that the model is wrong exactly, it's that two people can ask the same question in good faith and walk away with two different, equally confident-sounding answers, and there's no way to know which one is correct without checking the source yourself, which defeats the point of asking an AI assistant in the first place.

How do you make an LLM-powered feature more consistent in production?

• Push temperature as low as the task tolerates, and adjust only temperature or only top_p, not both at once, since stacking them makes the effect on output variance hard to reason about.
• Constrain the model with a structured output schema wherever the answer has a defined shape, so it's choosing between a small set of valid completions instead of free-generating prose, the same discipline covered in how to fix an AI agent that returns broken JSON.
• Ground the answer in a fixed, retrieved set of source documents rather than letting the model pull from its own parametric memory, so the specific text in front of it drives the answer, not whatever it half-remembers from training, the same grounding problem covered in why a RAG chatbot gives wrong answers.
• Run repeat and near-duplicate queries through an evaluation harness before shipping, so consistency is a measured number, not an assumption, following the same discipline as how to evaluate an AI agent before production.
• Cache the response for identical, high-stakes inputs at the application layer, so a repeated question returns the same reviewed answer instead of a fresh generation every single time.

How AIBOOTSTRAPPER solved this for a client

ComplySpark, the AI compliance document copilot AIBOOTSTRAPPER built, exists precisely because this consistency problem is a liability in a legal setting, not a curiosity. A drafting assistant that phrases the same policy clause two different ways across two documents doesn't just look sloppy, it creates a genuine compliance inconsistency an auditor can flag. Grounding every draft in the client's own policy library, with the sampling, schema and review discipline described above, is what gets the system to zero off-policy language slip-ups while still cutting document drafting time from days to an afternoon.

That same discipline is why AIBOOTSTRAPPER treats reproducibility as a design requirement, not a nice-to-have, on every AI product build where an answer needs to be trusted, not just plausible.

How AIBOOTSTRAPPER helps

If your AI feature gives a different answer depending on who asks or when, that's not a mystery bug, it's an unmanaged property of how LLMs generate text, and it's fixable with the right sampling, grounding and evaluation setup. AIBOOTSTRAPPER's AI product development team designs for reproducibility from day one on every agent and assistant we ship.

If you're about to put an LLM-powered feature in front of customers, employees, or auditors and you're not sure how consistent it actually is, book a call and we'll help you find out before your users do.

Sources and further reading

1. 1.Claude API Reference — Messages (temperature parameter)
2. 2.Thinking Machines Lab — Defeating Nondeterminism in LLM Inference