Practical Tuning Guide: Which Parameters to Change for Which Use Case
~15 min read
A concrete parameter cheat sheet across 3 common use cases — creative writing, factual QA, and code generation — showing which of the 7 levers actually matter for each, and in which direction to tune them.
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Key points
- •Creative writing: high temperature (0.8-1.0), generous top-p, moderate presence penalty, multinomial sampling
- •Factual QA/chatbots: low temperature (0.0-0.3), tight top-p, near-zero penalties, greedy-ish decoding
- •Code generation: low temperature like factual QA, but with tight stop sequences and generous max tokens to avoid truncated code
- •Temperature + sampling strategy is the primary diversity dial; penalties are secondary fine-tuning on top of that
- •Stop sequences and max tokens are about boundary control, not creativity — get these right regardless of use case