Constraining outputs and inputs

Constraining outputs and inputs in AI models refers to implementing limits or rules that guide both the data the model processes (inputs) and the results it generates (outputs). Input constraints ensure that only valid, clean, and well-formed data enters the model, which helps to reduce errors and improve performance. This can include setting data type restrictions, value ranges, or specific formats. Output constraints, on the other hand, ensure that the model produces appropriate, safe, and relevant results, often by limiting output length, specifying answer formats, or applying filters to avoid harmful or biased responses. These constraints are crucial for improving model safety, alignment, and utility in practical applications.