  Recently, Large Language Models (LLMs) have demonstrated remarkable
capabilities. Chain-of-Thought (CoT) has been proposed as a way of assisting
LLMs in performing complex reasoning. However, developing effective prompts can
be a challenging and labor-intensive task. Many studies come out of some way to
automatically construct CoT from test data. Most of them assume that all test
data is visible before testing and only select a small subset to generate
rationales, which is an unrealistic assumption. In this paper, we present a
case study on how to construct and optimize chain-of-thought prompting using
batch data in streaming settings.
