Answer: Low temperature and fixed random seed

## Explanation To achieve consistent, reproducible outputs across multiple calls to the same model with the same input, you need: 1. **Low temperature**: Temperature controls the randomness of the model's outputs. A low temperature (closer to 0) makes the model more deterministic and focused on the highest probability tokens. 2. **Fixed random seed**: The random seed ensures that any random processes in the model are initialized the same way each time, providing reproducibility. **Why other options are incorrect:** - **A) High temperature**: High temperature increases randomness and diversity in outputs, making them less consistent. - **B) Randomized seed**: Using different random seeds each time would produce different outputs even with the same input. - **D) High top-p value**: Top-p (nucleus sampling) controls the cumulative probability threshold for token selection. A high top-p value allows more diverse token selection, reducing consistency. **Key takeaway**: For reproducible results in generative AI models, use low temperature combined with a fixed random seed.

Author: Ritesh Yadav