Question

A team at a financial services firm is building a Foundry-based application that must solve complex multi-step compliance calculations where the reasoning path matters as much as the final answer. Auditors need the model to break problems into intermediate logical steps before producing a conclusion. During evaluation, the team notices that a standard chat completion prompt often jumps to answers that are numerically wrong on multi-part questions. Which approach should the team adopt to most reliably improve the correctness of these multi-step reasoning tasks?

Accepted Answer

Select a reasoning-optimized model and use chain-of-thought prompting that instructs the model to work through intermediate steps before stating the final answer.

Answer

Lower the temperature parameter to 0 and increase the max tokens so the model produces longer, more detailed answers.

Answer

Enable the content safety jailbreak shield so unsafe reasoning paths are filtered out before the answer is returned.

Answer

Increase the top_p (nucleus sampling) value to allow the model to explore a wider range of candidate tokens for each answer.