Many people, when encountering unsatisfactory performance from AI models, their first reaction is to criticize the algorithm itself. But think carefully, the model is actually just faithfully executing the "instructions" from the data — what it learns is what it will output.

If the final result looks very absurd? Then you need to trace back. Start by checking the data source. Is there a problem with the quality of the training set, or is there bias in the input features themselves? This shift in thinking will directly affect how you build the entire system. Instead of constantly tuning parameters, it’s better to focus more on data cleaning and preparation stages. Small changes can make a big difference.