New Approach for Debugging Large Language Models

Large language models (LLMs) have become integral to various AI applications, yet debugging remains a significant challenge. This article presents a systematic approach for LLM debugging, treating them as observable systems and offering structured methods that span issue detection to model refinement. By integrating evaluation, interpretability, and error analysis, practitioners can effectively diagnose weaknesses, refine model parameters, and adapt data for fine-tuning, even in cases lacking standardized benchmarks.