For a sentiment classification task (positive/negative/neutral), which prompt structure gives the MOST reliable and parseable output?
Select an answer to reveal the explanation.
Short Explanation
For classification, you constrain the output to exactly the label you need. Asking for one word from a defined list gives you machine-parseable output directly.
Full Explanation
For classification tasks in production, constrained output prompts are essential. Specifying the exact allowed output values and format, such as 'Respond with exactly one word: positive, negative, or neutral', produces outputs that can be parsed programmatically without ambiguity. This eliminates preamble, explanation, and variation in label format. The option that asks Claude to explain its feelings about the sentiment in detail generates free text requiring NLP parsing to extract sentiment, which is fragile. The option that has Claude write a one-paragraph essay on the sentiment detected produces verbose explanations instead of labels. The option that uses a very high temperature to get varied sentiment labels is counterproductive because high temperature introduces randomness, which is the opposite of what classification needs; low temperature is correct.