As large language models (LLMs) like GPT-4 become integral to applications which range from customer support to research and code generation, developers often face an essential challenge: troubleshooting GPT-4 output quality. Unlike traditional software, GPT-4 doesn’t throw runtime errors — instead it may provide irrelevant output, hallucinated facts, or misunderstood https://canvas.instructure.com/eportfolios/4096457/entries/14404190