Why I Don’t Trust LLMs to Decide When the Weather Changed
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A physicist-turned-ML-engineer argues against using LLMs to make threshold-based decisions in weather monitoring agents. Drawing on chaos theory and meteorological expertise, the author explains why probabilistic models shouldn't replace deterministic rules for well-defined decision boundaries. The post presents Skygent, an open-source weather change detection agent with a five-layer architecture where a Python evaluator handles all threshold logic and the LLM is invoked only to narrate already-made decisions. This hybrid approach enables 204 unit tests with zero LLM dependencies, reduces API calls from ~28 to 1-2 per week per event, and produces fully explainable, reproducible alerts. The core principle: use LLMs to explain decisions, not to replace well-defined ones.
Table of contents
The problem with “vibe-based” deltasMy path to this problemThe architectureWhy this architecture is testableEvent-Driven LLM InvocationA concrete exampleWhen this pattern breaksClosingSort: