A Systematic Approach for Large Language Models Debugging

• URL: http://arxiv.org/abs/2604.23027v1
• Date: Fri, 24 Apr 2026 21:30:33 GMT
• Title: A Systematic Approach for Large Language Models Debugging
• Abstract summary: Large language models (LLMs) have become central to modern AI, powering applications from open-ended text generation to complex agent-based reasoning.<n>This paper introduces a systematic approach for LLM debug that treats models as observable systems.<n>By unifying evaluation, interpretability, and error-analysis practices, our approach enables practitioners to iteratively diagnose model weaknesses.
• Score: 9.144050771609637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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