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, constrain the output to exactly the label you need — 'respond with one word: positive, negative, or neutral' 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 (e.g., 'respond with exactly one word from: positive, negative, neutral') produces outputs that can be parsed programmatically without ambiguity. This eliminates preamble, explanation, and variation in label format. Option A generates free text requiring NLP parsing to extract sentiment — fragile. Option C produces verbose explanations instead of labels. Option D is counterproductive — high temperature introduces randomness, which is the opposite of what classification needs (low temperature is correct).