Prompt engineering is voodoo. There's no sure way to determine how well these models will respond to a question. Of course, giving additional information may be helpful, but even that is not guaranteed.
Also every model update changes how you have to prompt them to get the answers you want. Setting up pre-prompts can help, but with each new version, you have to figure out through trial and error how to get it to respond to your type of queries.
I can't wait to see how bad my finally sort-of-working ChatGPT 5.1 pre-prompts work with 5.2.
It definitely isn’t voodoo, it’s more like forecasting weather. Some forecasts are easier to make, some are harder (it’ll be cold when it’s winter vs the exact location and wind speed of a tornado for an extreme example). The difference is you can try to mix things up in the prompt to maximize the likelihood of getting what you want out and there are feasibility thresholds for use cases, e.g. if you get a good answer 95% of the time it’s qualitatively different than 55%.
No, it's not. Nowadays we know how to predict the weather with great confidence. Prompting may get you different results each time. Moreover, LLMs depend on the context of your prompts (because of their memory), so a single prompt may be close to useless and two different people can get vastly different results.
