Some Random Thoughts is a personal blog maintained by Amrut Joshi, described simply as a place to put some random thoughts. The site collects short, focused essays rather than news or tutorials, and this particular post addresses a practical question in AI engineering: whether an LLM can be trusted to report its own confidence.
The core argument is that asking a model for a probability or confidence score produces a number, but not a justified one. The author first unpacks what confidence means using a weather forecaster example, where a 70% rain prediction reflects the forecaster’s information and uncertainty. He then explains why LLM-generated scores lack a comparable grounding, making them unreliable for agent designers who use them in decision logic.
The blog suits developers, researchers, and agent designers who are building systems that depend on model-reported certainty, as well as readers interested in clear-eyed critique of common LLM practices. It assumes some familiarity with LLM-based agents but keeps the reasoning accessible.
Use it as a short reference when designing confidence-based routing, escalation, or abstention logic in agent pipelines. The post is best read as an argument to verify confidence through external signals rather than model self-reports. Overall, it is a compact, well-reasoned note worth bookmarking for anyone who ships LLM agents.
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