What Makes LLMs Act Unpredictable? Understanding Non-Deterministic Inference

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The Big Question: Why Don’t Large Language Models Always Repeat Themselves?

So, you’ve used a large language model (LLM) like ChatGPT, Bard, or Gemini. You asked it a question twice. Sometimes, it gives you a different answer each time. That feels odd, right? We’re used to computers spitting out the same answer every time you ask the same thing. Not here.

This isn’t a bug. LLMs are built this way. There’s a reason behind the unpredictability. It’s not about the system being broken—or trying to play tricks on you. It’s just how they work.

And it helps to know why. Because, honestly, it explains a lot about LLMs and how we use them.

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What Does “Non-Deterministic” Really Mean?

You might hear the term “non-deterministic inference” a lot. All it means is this: the model doesn’t give you exactly the same output every time you ask the same thing.

A calculator is deterministic. 2 + 2 is always 4. But LLMs, well, not so much.

Non-determinism isn’t some fancy term meant to confuse. It’s just another way of saying, “You might get different results each time.” And that’s not only normal—it’s built in, on purpose.

Randomness is Baked In

The short version: LLMs use a touch of randomness when they reply. There’s a “temperature” setting when you run one. Crank up the temperature, and the answers get less predictable. Set it low, and things get more repeatable. But even at low settings, some randomness sneaks through.

Why do this? Because language isn’t like math. There are a hundred ways to say the same thing. If these models always answered in the exact same way, they’d sound boring, robotic, and wouldn’t handle new situations well.

Randomness keeps it fresh. Sometimes it’s a blessing (variety), other times a curse (inconsistent answers to the same question).

Each Word Depends on the Next

LLMs don’t answer in one big chunk. They predict one word at a time, then use that word to predict the next. It’s like telling a story, sentence by sentence, and making decisions as you go.

Try this: ask an LLM the same question a bunch of times. Notice how it might start the same, but half-way through, switch it up? That’s because every word adds a fork in the road. Tiny random choices in earlier words can set off a chain reaction. So, no two answers are exactly alike.

The Model Doesn’t “Remember” Past Sessions

People often think, maybe the system just remembers last time and tries to be different. It doesn’t. Each time you run a prompt, unless you’re in a special session, the model starts from scratch.

There’s no hidden list of your past questions. No “oh, you already asked that.” The reason for different answers is—again—randomness plus the chain reaction through each word.

Hardware and Timing Matter

Here’s something to chew on: Even if you use the same prompt, same settings, same everything, you might get a different answer depending on processing power, cloud load, or what else the server is doing. LLMs run across big computers in data centers, and little shifts in timing or hardware can push things a different way.

It’s not the biggest factor, but it counts. Sometimes it’s the butterfly effect in code—tiny differences create a new path for an answer.

If you want 100% repeatability, some providers let you “fix the random seed.” You have to ask for this though, and it’s rare outside of research settings.

Creativity at a Cost

Non-determinism means you get creative answers—more colorful language, more ways to describe things, sometimes surprising insights. This is great for brainstorming and new ideas.

But the trade-off? Less consistency. If you need the same result every single time—filling out legal forms, say—LLMs have to be locked down tight, or run with low temperature and high control.

Some paid services, like OpenAI’s ChatGPT Plus (currently around $20/month as of September 2025), allow you more control of things like temperature settings, history, and deterministic output.

Why Does This Matter for Regular Users?

Maybe you’re writing code, or maybe you’re just looking for a recipe. If you’re wondering why things don’t look the same each time, now you know: that’s how it’s built.

For some jobs, this is fine. For critical tasks, you might want something firmer. Tools like Google Gemini Advanced (about $30/month) let you adjust randomness. For pure repeatability, maybe stick to more traditional (deterministic) programs.

Remember, if the LLM ever “feels” a little inconsistent, it’s not broken—you’re just seeing the model do what it was designed to do.

Can You Make It Act Predictable?

Yes—sort of. Some platforms let you set temperature to zero. That’s as close as it gets to repeatable answers. But even then, sometimes you’ll spot little changes. If you need answers that are always exactly the same, it’s better to use rules-based systems.

And always check if your provider lets you set a “random seed” for your request. This helps, but it isn’t always perfect.

Takeaways: LLMs Don’t Always Repeat for a Reason

Large language models are different from old-school software. They don’t always say the same thing every time. That’s not a mistake—it’s a feature. It’s part of what makes them work in real life.

If you embrace this quirk, you’ll get the best out of these tools. Be flexible, and know what to expect. LLMs might surprise you—but now, you know why.