An e-commerce company uses Claude to generate product descriptions. They set temperature=0.9 for creative variation and are happy with output diversity. However, they notice that 8% of outputs contain factual errors about product specifications (wrong dimensions, incorrect materials). Their product spec data is provided in the prompt. What parameter adjustment most directly reduces factual errors while preserving reasonable output diversity?

Factual errors in spec-grounded generation are often caused by high temperature pushing the model toward less-probable tokens — including hallucinated or misremembered specifications. Reducing temperature from 0.9 to 0.3–0.5 substantially reduces hallucination risk while still allowing stylistic variety in word choice and sentence structure. Paired with an explicit grounding instruction, this addresses both the probabilistic and instructional causes of factual errors. Option A (temperature=0.0) would likely over-correct, producing repetitive outputs with insufficient variety — and deterministic generation doesn't guarantee factual accuracy, as the most probable token can still be wrong. Option C (top_p adjustment) has less direct effect on factual grounding than temperature; top_p mostly affects output diversity in the tail distribution. Option D (verification pass) is a valid safety net but adds latency and doubles cost — it treats the symptom rather than the cause.