Identifying where faults occur is an essential part of debugging, yet examining extensive system logs can be slow and mentally demanding, especially in complex software environments. One emerging strategy to enhance log analysis is to employ large language models (LLMs) to distill log information into more manageable summaries that can guide human reasoning during diagnosis. We report on a case study carried out in an automotive setting, where engineers investigated actual failures with and without support from an LLM-based summarization tool. During fault localization sessions where participants analyzed real failure logs, we collected cognitive load measurements, observed their reasoning processes, and gathered feedback on both the LLM-based summarization and the workflow through post-session interviews. Our results indicate that although the use of summaries raised certain cognitive demands, particularly related to mental effort and time pressure, participants experienced less frustration overall and considered the support helpful in focusing their attention. They also expressed a clear interest in being able to shape and refine summaries as their understanding evolved. These findings offer insights into how LLM-generated summaries influence practitioners’ diagnostic work and point toward the need for more adaptive, interactive, and workflow-aware support.