  Large language models (LLMs) have shown great progress in responding to user
questions, allowing for a multitude of diverse applications. Yet, the quality
of LLM outputs heavily depends on the prompt design, where a good prompt might
enable the LLM to answer a very challenging question correctly. Therefore,
recent works have developed many strategies for improving the prompt, including
both manual crafting and in-domain optimization. However, their efficacy in
unrestricted scenarios remains questionable, as the former depends on human
design for specific questions and the latter usually generalizes poorly to
unseen scenarios. To address these problems, we give LLMs the freedom to design
the best prompts according to themselves. Specifically, we include a hierarchy
of LLMs, first constructing a prompt with precise instructions and accurate
wording in a hierarchical manner, and then using this prompt to generate the
final answer to the user query. We term this pipeline Hierarchical Multi-Agent
Workflow, or HMAW. In contrast with prior works, HMAW imposes no human
restriction and requires no training, and is completely task-agnostic while
capable of adjusting to the nuances of the underlying task. Through both
quantitative and qualitative experiments across multiple benchmarks, we verify
that despite its simplicity, the proposed approach can create detailed and
suitable prompts, further boosting the performance of current LLMs.
