A Systematic Approach for Large Language Models Debugging
For practitioners deploying LLM-based systems, this work provides a structured methodology to address the persistent challenge of debugging opaque and probabilistic models, but it is primarily a conceptual framework without empirical validation.
This paper introduces a systematic, model-agnostic approach for debugging large language models, unifying evaluation, interpretability, and error analysis to enable iterative diagnosis and refinement. The approach aims to accelerate troubleshooting and improve reproducibility, transparency, and scalability in LLM deployment, though no concrete performance numbers are provided.
Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging these models remains a persistent challenge due to their opaque and probabilistic nature and the difficulty of diagnosing errors across diverse tasks and settings. This paper introduces a systematic approach for LLM debugging that treats models as observable systems, providing structured, model-agnostic methods from issue detection to model refinement. By unifying evaluation, interpretability, and error-analysis practices, our approach enables practitioners to iteratively diagnose model weaknesses, refine prompts and model parameters, and adapt data for fine-tuning or assessment, while remaining effective in contexts where standardized benchmarks and evaluation criteria are lacking. We argue that such a structured methodology not only accelerates troubleshooting but also fosters reproducibility, transparency, and scalability in the deployment of LLM-based systems.