When Large Language Models return “Inconclusive” in classification tasks, practitioners are left without insight into what went wrong. This diagnostic gap can delay medical decisions, undermine content moderation, and mislead downstream systems. We present FLARE (Failure Location and Reasoning Evaluation), a framework that transforms opaque failures into seven actionable categories. Applied to 5,400 election-misinformation classifications, FLARE reveals a surprising result: Few-Shot prompting—widely considered a best practice—produced 38× more failures than Zero-Shot, with 70.8% due to simple parsing issues. By exposing hidden failure modes, FLARE addresses critical misunderstandings in LLM deployment with implications across domains.