That makes sense, and I’m guessing that a two-shot approach will work much better than a single-shot approach, absolutely. You could also provide a sample set that represents the diversity and depth of typical questions (if possible) to the GPT, and get IT to generate a “reasoning blueprint” or “answer formatting blueprint” for itself, which would likely, if possible to generalize your use case to a significant extent, could return you to a single-shot model.
I.e. if you provide enough sample data and get the GPT to “come up with best practices for robustly answering the questions in a single shot” by "giving it more context about what kind of responses you are looking for, even up to possibly standardizing output format in a conceptual way, not necessarily in a structure way (gpt works great when giving a “form to fill in with details” essentially).
Then you upload that “guidelines documentation” that you had the GPT produce from your sample-case scenarios and use it within System Instructions or Developer Message depending on your systems use of the API
Then, test your questions again, and see what kind of results you are getting.
Usually in building end-user systems you are going to want to avoid multi-turn, because that takes much longer than say, even passing in up to several thousand input tokens of “contextual awareness” within the developer message/system instructions. Previously my advice to you was thinking this was your own personal use of the GPT, not a public-facing system with end users.
In this case, I would then recommend that you spend your time working with the GPT to analyze your normal use case scenarios, and essentially “outline the desired response style/format/level of detail, etc.”.
Then, you simply provide the information to the GPT along with the users question - and voila - in single shot you get most likely exactly what you would have gotten during multi-shot with the “warm up”.
Or probably even better, if you spend your time robustly developing the system instructions/developer message that will be sent to the GPT.
Remember, input tokens are cheap. We are talking .001 - .01 for several thousand tokens.
For example I routinely ask questions of the GPT in a relatively simple way as a “prompt”, but I provide it with a 20k-50k context window of documentation and instructions!
And then you get extremely good results.