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 Type…


This content originally appeared on DEV Community and was authored by Kuldeep Paul

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.


This content originally appeared on DEV Community and was authored by Kuldeep Paul


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