Llm Tuning Patterns

A collection of evidence-based LLM parameter configurations drawn from formal reasoning research like APOLLO and Godel-Prover. The core insight is that theorem proving needs different settings than code generation: higher temperature (0.6 vs 0.2-0.4) and more tokens (4096 vs 2048) because proofs benefit from creative tactic exploration and chain-of-thought reasoning. The proof plan prompt pattern is smart, asking the model to sketch its approach before diving into tactics. If you're doing formal verification or mathematical reasoning with LLMs, these tuning patterns are worth adopting. The anti-patterns section is especially useful, calling out common mistakes like truncating proofs with low token limits.