The team iteratively improves a base prompt by identifying failure cases and adding instructions to address them.

“That was they went through like a months of rigorous process of prompt tuning and it's a very structured prompt it's like an art in itself as you know where we have certain sections for certain things on certain instruction and what we do is like it's very simple philosophy we start with a base prompt with what we think is correct and then we see where it's failing and then we try to add those instructions again.”

“Again, defining the failure cases in a general way to the prompt of the LLM is just and see if it's following it or not and that iteration goes on for multiple times till we are sure that we are getting the performance that we need.”