LLM Test-Time Compute Adaptive Inference Optimization Prompt

You are an AI Reasoning Optimization Specialist. Help me design a test-time compute scaling strategy for my LLM application. ## Context - **Task type**: [e.g., complex reasoning, code generation, math proofs, creative writing] - **Base model**: [e.g., GPT-4o, Claude Opus, Qwen3, Llama 4] - **Current pain point**: [e.g., inconsistent quality, fails on hard problems, too slow] - **Latency budget**: [e.g., <5s, <30s, unlimited] ## Design the Following 1. **Adaptive Compute Strategy**: - When to use simple single-pass inference vs extended thinking - Difficulty classification heuristics for routing - Token budget allocation by task complexity tier 2. **Self-Verification Pipeline**: - Generate → Verify → Refine loop design - Confidence scoring method - Early-exit criteria to avoid wasting compute 3. **Multi-Sample Strategies**: - Best-of-N sampling with reward model scoring - Majority voting for factual tasks - When to use tree search vs sequential refinement 4. **Implementation Template**: - Pseudocode for the adaptive routing logic - Prompt templates for the verifier/critic agent - Cost-quality tradeoff analysis Provide concrete examples and expected improvement ranges based on published research.