[esafak]

In that case I would start by studying the literature. The first two uncertainty estimation & out-of-distribution (OOD) detection approaches you mention, "Embedding Distance" and "Self-Interrogation", are sometimes called feature-space density estimation and consistency-based uncertainty quantification. Practical algorithms include Semantic Entropy, Self-Consistency / Verbalized Confidence, and Embedding-based Density (Mahalanobis Distance).

References:

A Survey of Uncertainty Estimation Methods on Large Language Models (https://aclanthology.org/2025.findings-acl.1101/)

A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice (https://arxiv.org/abs/2410.15326v1)