The Problem of Consistency With AI (And How To Fix It)

As an AI Trainer I often get to see how the same prompt and information can generate different results. Often students will comment:

“I used the same prompt, with the same data but the answers are different.”

Sometimes the response is broadly the same but phrased differently. Sometimes the emphasis changes. And occasionally if doing data analysis, the data itself is wrong. This is despite very clear and detailed prompts and the exact same data.

Over time, this leads to a bigger issue. People start to lose confidence in the tools they’re using. And if you don’t trust the output, AI quickly becomes more of a novelty than something you’d rely on for real work. So why does this happen?

Why AI Is Inconsistent

The first thing to understand is that general purpose tools like ChatGPT don't work like traditional software. It’s not a spreadsheet. It’s not a database. And it’s definitely not a calculator.

Probabilistic Nature

At its core, Generative AI works on probability. It is constantly predicting the most likely next word, sentence or line of reasoning based on patterns it has learned. If it reasons or problem solves it have to decide what to do and then do it. The important thing to understand is that it will almost always find a different way to an outcome, especially when generating a textual output.

That means there is often more than one valid way to answer the same question. The model can take different paths through the same problem and still arrive at an answer that looks reasonable.

Creativity and Temperature

Another factor is creativity. Most standard chat interfaces run at a relatively high “temperature” setting. In simple terms, this allows the model to be more flexible and human-like in how it responds. That’s great for creativity, but the cost is consistency. The more creative you allow a system to be, the less predictable it becomes.

Server Resource

AI companies are struggling to keep up with demand. When systems are under heavy load, small inconsistencies and errors tend to increase. This doesn’t mean the model is suddenly bad, it just means you’re seeing the limits of a system being used by millions of people at once. OpenAI is actually quite transparent about this, and you can see how ChatGPT is "feeling" on any given day via their public status page.

And lets not forget this is a company that until recently advertised "Priority during busy periods" on most of it's plans, the more you paid, the less likely your access would be impacted during busy periods! I suspect this is still the cases but they have quietly removed this from their pricing page.

So How Can You Fix This AI Consistency Issue?

The good news is that while you’ll never eliminate variation completely, you can reduce it significantly with better prompting and better expectations.

1. Define your outputs

   When prompting you can define your output and be very specific. For example if you data analysis to be very similar each week then make sure your output is clearly defined. A top tip is to ask AI to review any examples of your output and describe it so that you can use that in your prompt!

   
   

2. Use Custom Instructions

   Custom instructions are another underused feature. Asking for a factual, cautious tone and prioritising accuracy over creativity can dramatically improve reliability. You can also explicitly ask the model to flag uncertainty rather than guessing something that’s particularly important when working with data.

1. Build Custom Tools

   Custom Tools allow you to define and update specific processes and instructions so that when you ask for an output AI will already know what it needs to do, the process it needs to follow and the output expected of it. We can teach anyone with no AI knowledge how to build a Custom Tool in a day. The scope for these tools is incredible, and often students eyes light up when they realise what is possible!

1. Build tools that bypass the interface

   If consistency really matters — for example in repeatable workflows or internal tools — the API is often a better option. It allows temperature to be reduced to zero, producing far more predictable outputs. Even then, it’s worth noting that variations can still occur, but they are much less pronounced than in the standard chat interface.

1. Use detailed prompts with great examples

   Above all clear instructions (prompting) coupled with great examples is key to getting better results overall. Remember it's always better to provide the data to your tool than to ask it to research or find it and then analyse. Making the job as easy as possible for the tool will vastly improve accuracy and results.

A More Realistic Expectation

The key thing to remember is that generative AI will never be perfectly consistent.  But understanding why variation happens, reducing it where it matters, and designing prompts and workflows that take it into account can make a real difference.

In practice, one of the most effective ways to improve reliability is to be crystal clear about the output you want. Do that, and AI becomes far more useful and far less frustrating.

If you’d like to explore this in more depth, or understand how to use AI confidently and responsibly in a business setting, take a look at our AI training courses or get in touch to see how we can help.