Have you tried tweaking parameters like temperature, top_p, or seed value when sending the API request?
Beyond that and due to the probabilistic nature of the LLM response I'm not sure how a reproducible "matching" between chat interface and API could be achieved.
I'm working primarily with the API through my own wrapper and I noticed that I tend to give less detailed instructions than when I'm using the OpenAI chat interface often resulting in a less accurate response.
Beyond that and due to the probabilistic nature of the LLM response I'm not sure how a reproducible "matching" between chat interface and API could be achieved.
I'm working primarily with the API through my own wrapper and I noticed that I tend to give less detailed instructions than when I'm using the OpenAI chat interface often resulting in a less accurate response.