How to Reliably Gauge LLM Confidence?

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I’m trying to estimate an LLM’s confidence in its answers in a way that correlates with correctness. Self-reported confidence is often misleading, and raw token probabilities mostly reflect fluency rather than truth.

I don’t have grounding options like RAG, human feedback, or online search, so I’m looking for approaches that work in this constraint.

What techniques have you found effective—entropy-based signals, calibration (temperature scaling), self-evaluation, or others? Any best practices for making confidence scores actionable?