Before the widespread use of large, general-purpose models, a common approach for handling complex questions was to use a specialized model to break them down into simpler sub-questions. This could be done in two primary ways: generating all sub-questions at once (batch generation) or generating them one by one, using the answer to one to help form the next (sequential generation). Analyze the trade-offs between these two approaches. In your analysis, consider aspects like error propagation, computational efficiency, and the ability to adapt the decomposition process based on intermediate findings.