LLM output is non-deterministic: what that means and why it matters

LLM output is non-deterministic: the same prompt, sent to the same model with the same settings, can return a different answer each time. Language models write by choosing each next word from a range of likely options, so some variation is built in. Even at a temperature of 0, the major providers do not guarantee identical results. For businesses, that variation is useful when drafting and a risk wherever content must be consistent, so production use needs clear instructions, automatic checks and human review.

Originally published May 2023. Updated September 2026 with current provider guidance on temperature, seeds and reproducibility.

Picture this: You’re using a large language model (LLM) like ChatGPT to generate a product description for your online store. You input the same prompt twice, expecting to receive identical results each time. But, to your surprise, the outputs are entirely different! In some cases, you may see artifacts that don’t even make sense, as if the LLM is making things up.

This unpredictable behavior is due to the non-deterministic nature of LLMs, which can be both a blessing and a curse in content creation.

What non-determinism means in LLMs

Non-determinism, in the context of LLMs, means that the model can produce different outputs even when given the same input. A model generates text one piece at a time, and at each step it has a probability for every possible next word. Rather than always taking the single most likely word, it samples from those options. That is what makes the output varied, and often more natural.

Settings such as temperature control how adventurous that sampling is. Turning temperature down makes output more predictable, but it does not make it fully repeatable. Anthropic’s API documentation states that “even with temperature of 0.0, the results will not be fully deterministic.” OpenAI describes its seed parameter as “best effort”: repeated requests should mostly match, but determinism is not guaranteed, and backend changes can alter results. Research from Thinking Machines Lab (September 2025) traced much of the remaining variation to server load: the number of requests processed together changes the arithmetic slightly, and small differences can change which word comes next.

The risks of non-determinism

For businesses like Sitation, which rely on LLMs to generate product descriptions and other content, non-determinism can be a double-edged sword. On the one hand, it allows for a broad range of responses and can inject creativity into the content generation process. On the other hand, inconsistency can lead to confusion and frustration for both the business and its customers.

Imagine the confusion when a customer notices two different product descriptions for the same item, or when a marketing team member needs to approve content, but they receive varying outputs each time they run the same prompt. These scenarios can harm a company’s brand image and credibility, making non-determinism a serious concern for businesses. Too many creative liberties also make it more likely that the model will “hallucinate”: state things that were not part of the original prompt or product data.

Non-determinism is also a good thing

There’s also a major benefit to non-determinism. A degree of randomness adds a creative spark to the output, and allows you to quickly iterate if you don’t like the initial draft. Sitation’s AI content product, Plezio Draft (formerly RoughDraftPro), makes extensive use of this by allowing a rapid-fire rewrite of AI-generated product content, including titles, short descriptions, feature bullets, or entire PDPs.

Temperature is the setting most people use to control this. Perhaps a better way to think of it is this: when we dial it up, we let the model take more risks. In doing so, sometimes it will come up with something better. Turning temperature all the way down makes the output far more mechanical, and more consistent. Note that some newer models no longer let you change it. Anthropic’s documentation, for example, says models released after Claude Opus 4.6 do not support setting temperature (checked September 2026). On those models, consistency has to come from the instructions and checks around the model.

How to get more consistent LLM output

While it’s impossible to eliminate non-determinism entirely, these strategies help you achieve consistent results when using LLMs for business content:

1. Craft specific prompts: Provide clear and detailed instructions, specifying the format and content you expect. This narrows the range of potential outputs.
2. Use templates: Create a template with placeholders for the variable parts of your content. The structure stays consistent while the LLM fills in the specific details.
3. Ground the model in your product data: Supply the attributes from your PIM or MDM system in the prompt and tell the model to use only those facts. A model that isn’t guessing has less room to vary.
4. Check output automatically: Validate each result against rules, such as length limits, required attributes and banned claims, and keep a fixed set of test products to re-run whenever you change a prompt or model.
5. Iterative refinement: Instead of relying on a single pass, refine the output by providing feedback and adjusting the prompt.
6. Human in the loop: Combine the power of LLMs with human expertise. Have a person review and approve generated content before it is published.

Plezio Draft applies these ideas so the technology can be used in a production environment for high-volume content creation: it works from the product record in Akeneo or Salsify, uses prompts tuned to your brand and channels, and has your team approve results before they are saved back to your PIM. Learn more about Plezio Draft.

Frequently asked questions

What does “non-deterministic” mean in the context of generative AI?

It means the same input can produce different outputs. A generative AI model samples each next word from a set of likely options, so running an identical prompt twice may give two different answers. A deterministic system, by contrast, always returns the same output for the same input.

Is an LLM deterministic at temperature 0?

Not reliably. A temperature of 0 makes output much more consistent, but providers including Anthropic and OpenAI state that identical results are not guaranteed. Differences in how requests are processed on the provider’s servers, and updates to the model or its infrastructure, can still change the output.

Why do LLMs give different answers to the same prompt?

Mainly because of sampling: the model chooses among likely next words rather than always taking the top one, and the temperature setting controls how much it varies. Server-side factors, such as how many requests are processed together, and model updates add further variation.

Is non-determinism the same as hallucination?

No. Non-determinism is variation between answers; hallucination is an answer that states something false or unsupported. They are related, because higher randomness gives the model more room to invent details, but a model can be perfectly consistent and still wrong. Grounding the model in verified product data addresses hallucination; the consistency steps above address variation.

How do you get consistent AI-generated product content at scale?

Give the model specific instructions and a template, supply the facts from your product data, validate every result automatically, and keep a person approving what goes live. Sitation designs this kind of workflow in its AI digital transformation services and runs it on your product data with Managed Agents for PIM & MDM.

Conclusion

Large language models and their APIs have changed the way businesses generate content. However, their non-deterministic nature can pose challenges when consistency is crucial. By understanding what causes the variation and building controls around it, you can harness the full potential of LLMs and create high-quality, consistent content for your business. If you would rather have agents run this work on your product data, see Managed Agents for PIM & MDM.

Steve Engelbrecht is CEO and founder at Sitation. Follow Steve on LinkedIn for more insights on leveraging artificial intelligence for e-commerce applications.