Why doesn't AI always give me the same answer?

I get this question a lot from my clients: "Why doesn't this thing (AI) give me the same answer when I ask it the same question? Is it stupid?"

That's a pretty good question to ask if I am honest. However, this misses the entire point of Generative AI technologies: it will always give you a different answer, even for the same question, because it is non-deterministic!

"Generative AI is non-deterministic"

But what does this mean?

Understanding non-determinism in business terms

Non-determinism in AI means that when you provide the same prompt to a system multiple times, you receive different but valid responses. Think of it like asking three expert consultants the same strategic question - each provides different but valuable perspectives based on their expertise and approach.

"You wouldn't want three expert consultants to give you identical advice - that defeats the purpose of getting multiple perspectives."

Although this may feel counterintuitive, imagine if GenAI algorithms were just like traditional algorithms that, given the same inputs, they will always produce the same output.

Take the example below (I hope my non-technical readers can follow along):

// This function adds two numbers up
function add(a, b) {
  return a + b;
}

The function add is an example of a deterministic algorithm. When computer executes this function with say add(2,2) the output will always be 4.

Now, imagine if GenAI used the same algorithms and frameworks of deterministic systems - that would kill creativity and problem-solving, which I think are the greatest principles behind AI. Turing designed his famous test in the quest of understanding if machines could be "intelligent". My question to you is: how can machines or humans alike be intelligent without creativity and problem-solving capabilities?

"Creativity and problem-solving are integral aspects of intelligence."

Now that we understand the importance of non-determinism in AI systems, let's take a look at the controls behind AI creativity.

The Statistical Controls Behind AI Creativity

Large Language Models (LLMs) have 3 primary user-facing controls that influence the creativity, randomness and determinism of responses:

Temperature

Temperature controls the degree of randomness in token selection. If we continue with our expert consultants analogy, this is like asking different expert consultants the same question:

• low temperature = conservative consultant
• high temperature = creative brainstormer

Top-K Sampling

Top-K sampling limits the number of tokens the model considers at each step to the top k most likely ones. As statistics hurt my brain, imagine our consultant brainstorms a list of possible answers, ranked from most likely to least likely.

Top-K is like telling them:

“Only choose from your top k favourite ideas, no matter how close the others are.”

So if k = 5, they throw away everything outside their top 5 picks — even if idea #6 is just slightly less likely.

The effect we get is answers are limited to a fixed shortlist, which keeps things focused but might miss out on some good, slightly less obvious options.

Top-P (Nucleus Sampling)

Top-P or nucleus sampling includes the smallest set of tokens whose cumulative probability exceeds a threshold p. Again, let's use our analogy to understand these difficult (for me at least) statistical concepts:

Instead of a fixed number, Top-p says:

“Only consider ideas until your total confidence in them reaches p (say 90%). Ignore the rest.”

So if our consultant's top 3 ideas already cover 90% of the their confidence, they would discard the rest - even if they had prepared 20 more. But if ideas are equally uncertain, they might include 10 - 15 to hit that same 90%.

The effect we get here is a smarter, more flexible list, based on how confident they are in their ideas, not just a fixed count.

So let's summarise all of that in a table:

It is worth noting that even though LLMs do expose these controls do end users, their responses will still vary and at times, be totally unpredictable.

Now that we understand why AI behaves this way, the real question becomes: How do we harness this variability for business value? The answer depends on your context.

Practical Strategies for Working with Non-Deterministic AI

Instead of fighting AI's variability, here's how to make it work for your business:

1. Use variability for ideation, not execution Ask AI the same question 3-5 times when brainstorming. Each response gives you different angles on the problem. Then YOU choose which path to execute.

2. Create feedback loops When AI gives you an answer you like, ask it to expand on that specific approach. This narrows the variability while maintaining creative problem-solving.

3. Set clear evaluation criteria Before prompting AI, define what "good" looks like. This helps you quickly assess which variations are useful, turning unpredictability into options.

4. Document winning patterns Keep a library of prompts and approaches that consistently deliver value. While outputs vary, certain prompt structures produce more reliable results.

5. Combine AI responses with human judgment Treat AI outputs as ingredients, not finished meals. The value comes from how you combine and refine multiple responses.

This approach works whether you're writing marketing copy, designing systems, or solving complex business problems. The key is shifting your mindset from AI should give me THE answer to AI gives me OPTIONS to choose from.

"The key is shifting your mindset from 'AI should give me THE answer' to 'AI gives me OPTIONS to choose from'."

Conclusion

So when your client asks 'Why doesn't AI give me the same answer?' - now you can explain it's not a bug, it's the feature that makes AI valuable for solving real business problems.

You wouldn't want your best consultant to give the same advice for every problem. Intelligence - artificial or otherwise - requires the ability to adapt, create, and explore multiple solutions.

Your role as a leader is to build the frameworks that channel that creativity toward business value. Because in 2025, the question isn't whether AI will be part of your business - it's whether you'll master its uncertainty before your competitors do.