To influence the output of the ChatGPT API, you can use several parameters:
1. `messages`: It is an array of message objects. Each object includes a role (“system”, “user”, or “assistant”) and the content of the message from the role. The user’s message is used as input to the model.
1. `temperature`: It controls the randomness of the model’s responses. Higher values (e.g., 0.8) make the output more random, and lower values (e.g., 0.2) make it more deterministic.
1. `max_tokens`: This determines how long the model’s response will be. Setting this parameter to a specific number will limit the response to that number of tokens.
1. `top_p`: It can be used in place of temperature to use nucleus sampling in which the model chooses from the smallest possible set of tokens whose cumulative probability exceeds the threshold `top_p`.
1. `frequency_penalty`: It penalizes new tokens based on their frequency, giving you control over the model’s likelihood of generating frequent/plain tokens. Set it between -2.0 (encourage frequent tokens) and 2.0 (discourage frequent tokens).
1. `presence_penalty`: It penalizes new tokens based on their presence, giving you control over the model’s likelihood of generating novel tokens. Set it between -2.0 (encourage novel tokens) and 2.0 (discourage novel tokens).
Remember that these parameters might require some experimentation to get the desired generation style for your use case.
