A Systematic Approach for Large Language Models Debugging

A new paper from IBM Research, authored by Basel Shbita and 12 colleagues, introduces a systematic approach for debugging large language models (LLMs). Published on arXiv (2604.23027), the work addresses the persistent challenge of diagnosing errors in these opaque, probabilistic systems. The methodology treats LLMs as observable systems, providing structured, model-agnostic techniques from issue detection to refinement. It unifies evaluation, interpretability, and error-analysis practices, enabling practitioners to iteratively diagnose weaknesses, refine prompts and parameters, and adapt data for fine-tuning or assessment—even in contexts lacking standardized benchmarks.

This approach accelerates troubleshooting while fostering reproducibility, transparency, and scalability in LLM deployment. By moving beyond ad-hoc fixes, it offers a reliable framework for improving model performance across diverse tasks, from open-ended generation to agent-based reasoning. The paper is a significant step toward making LLM debugging more systematic and less reliant on trial-and-error, benefiting both researchers and engineers building production systems.