Hello to the community !
Video Game
Imagine you are developing a video game which change the scenario in real time by using and collecting data on the player and the game.
The collection is made for example when the player is talking to AI characters in the game.
They might say, “I don’t think those kind of characters funny anymore”, “this task is too hard not funny” or “There is too much zubats in this cave!”.
Huge nested dict
There is a huge nested dict in the background which contains information about the user and the game; [user], [user][quest] (which quest he has done or he has tried, how does it rate the difficulty, …), [user][PNJ] (what how many time he talk to a pnj, , which one he fights, the win, the loss, …)
Three API Calls
This huge nested dict is going to be update by AI using the recommendation of a previous call.
First call : Analyze update raw data collecting during the game and provide orders (for example it might order : decrease the spawn rate of zubats! And make the gym leader difficulty rate higher).
Second call : Analyze orders and fetch current relevant data to feed the third call (the dataframe size is prohibitive to add to the third prompt.
Third call: Using llm function callings (with a lot of functions) the change are applied. For example the AI will update the spawn rate key.
Efficiency question
The goal to lessen the number of errors (when the first call order some change, that could be done, but are not done).
Any advice ?
Thank you for your help.