This study investigates the practical performance and limitations of the multilingual pre-trained model Whisper in low-resource language settings, using a Hakka speech recognition challenge as a case study. In the preliminary phase, our team (Group G) achieved official scores of 75.58% in Character Error Rate (CER) and 100.97% in Syllable Error Rate (SER). However, in the final phase, both CER and Word Error Rate (WER) reached 100%. Through a retrospective analysis of system design and implementation, we identified three major sources of failure: (1) improper handling of long utterances, where only the first segment was decoded, causing content truncation; (2) inconsistent language prompting, fixed to “Chinese” instead of the Hakka target; and (3) lack of systematic verification in data alignment and submission generation, combined with inadequate evaluation setup.Based on these findings, we propose a set of practical guidelines covering long-utterance processing, language consistency checking, and data submission validation. The results highlight that in low-resource speech recognition tasks, poor data quality or flawed workflow design can cause severe degradation of model performance. This study underscores the importance of robust data and process management in ASR system development and provides concrete insights for future improvements and reproducibility.