Debugging LLM Failures: A Practical Guide

Introduction

Large Language Models (LLMs) power many applications, but they sometimes produce hallucinations, incorrect reasoning, or policy violations. Systematic debugging is essential to maintain reliability.

Common Failure Types

• Hallucinations – fabricated facts.
• Reasoning errors – broken logical chains.
• Tool misuse – incorrect function calls.
• Safety issues – policy violations.

Observability Setup

1. Tracing – capture prompts, responses, token usage, and tool calls.
2. Structured logging – store full conversation, model parameters, and metadata.
3. Real‑time alerts – monitor latency, error rates, and quality scores.

Debugging Workflow

1. Reproduce – collect failing examples and create minimal reproductions.
2. Root‑cause analysis – inspect traces, context windows, and tool interactions.
3. Fix – refine prompts, add guardrails, adjust model settings, or redesign the workflow.
4. Validate – run regression and edge‑case tests, measure performance impact.

Conclusion

By combining thorough observability with a disciplined debugging process, teams can quickly identify and resolve LLM failures, leading to more trustworthy AI systems.