This article proposes a sensemaking methodology to enhance human-in-the-loop technical and professional communication (TPC) practices when working with generative artificial intelligence (GenAI) output, which is often ambiguous and not always accurate. Sensemaking describes actions and cognitive strategies humans use to make sense of new/ambiguous information. We argue that sensemaking can help TPC students navigate making sense of GenAI output for better judgment in evaluating AI output. Particularly, we leverage sensemaking's Situation-Gap-Bridge-Outcome framework as a heuristic to identify situational contexts outside of GenAI, gaps in knowledge, create bridges for those gaps, and evaluate outcomes and connect this to extant TPC literature and discuss its implications.
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