A support team sends the same prompt to a hosted LLM many times a day and gets noticeably different wording each time. They want answers to stay closely on-message and vary far less, without demanding byte-for-byte identical output. Which change best achieves this?
- ALower the temperature toward 0.2 so the model concentrates on the highest-probability tokens. Correct
- BRaise the temperature toward 1.0 so the model considers a wider spread of next tokens.
- CIncrease the maximum output-token limit so each answer has room to finish completely.
- DRaise top-p toward 1.0 so the model samples from the full probability distribution.
Why A is correct: Correct. A low temperature sharpens the distribution toward the likeliest tokens, so wording stays consistent run to run while still allowing slight variation.
Why B is wrong: Higher temperature flattens the token distribution and increases variation, which moves away from the on-message consistency the team asked for.
Why C is wrong: The token limit governs how long an answer can be, not how much its wording varies, so consistency is unchanged.
Why D is wrong: A top-p of 1.0 keeps the entire distribution in play, which preserves or increases variation rather than reducing it.