Knowledge boundary probing and demand-guided intervention for LLM-based power system code generation
The research addresses LLM failures in generating power system code due to differing API knowledge between models. It proposes a three-part system: benchmarking tasks, profiling model API knowledge through documentation probes, and an intervention mechanism that proactively injects relevant documentation into prompts or reactively provides execution traces upon failure.