Large language models (LLMs) struggle on processing complicated observations in interactive decision making. To alleviate this issue, we propose a simple hierarchical prompting approach. Diverging from previous prompting approaches that always put the \emph{full} observation~(\eg a web page) to the prompt, we propose to first construct an action-aware observation which is more \emph{condensed} and \emph{relevant} with a dedicated \summ prompt. The \actor prompt then predicts the next action based on the summarized history. While our method has broad applicability, we particularly demonstrate its efficacy in the complex domain of web navigation where a full observation often contains redundant and irrelevant information. Our approach outperforms the previous state-of-the-art prompting mechanism with the same LLM by 6.2\% on task success rate, demonstrating its potential on interactive decision making tasks with long observation traces.

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