I was working with a colleague recently on a project involving the use of the OpenAI API.

I brought up the idea that, perhaps it is possible to improve the accuracy of API response by modifying the API call to increase the amount of reasoning performed.

My colleague quickly asked ChatGPT if this was possible, and the answer came back “No, it’s not possible to do that.” then I asked essentially the same question to my own instance of ChatGPT, and the answer was, “Yes, you can do it, but you need to use the OpenAI Responses API.”

How did we get such different answers? Was it the wording of the prompt? Was it the custom instructions given in the account personalization, where you describe who you are and how you want ChatGPT to respond? Is it possibly different conversation history? Many factors could have contributed to the different response. Unfortunately, many of these factors are either not easily controllable at the user level or not convenient to change to alternatives in a protracted trial and error search.

I’ve had other times when I will first get a highly standardized, generic answer from ChatGPT, even in Thinking mode, that I know is not quite right or just seems off. Then when I push back, I may get a profoundly different answer.

It’s simply a fact that large language models are conditional probabilistic systems that do not guarantee reproducibility in practice, even given the same inputs, even at temperature=0 [1]. Their outputs depend sensitively on prompt wording, context window contents, system instructions, and model configuration. Small differences in these inputs can yield substantially different outputs.

How well an AI chatbot responds can obviously have a massive impact on how effective the tool will be for your use case. Differences in responses could materially affect the outcome of your project. I take this as a wake-up call to be persistent, vigilant and flexible in attempts to obtain reliable answers from these new AI tools.

Notes

[1] (some) sources of nondeterminism: floating point / GPU nondeterminism, differing order of operations from distributed collectives, ties or near-ties in token probabilities, backend/infrastructure changes, model routing, hidden system prompt differences or tool availability.