Prompt Debugging

Introduction

Prompt debugging is the targeted process of finding why a specific prompt failed on a specific input. Different from eval — eval is bulk, debug is surgical.

Beginner analogy: Like using a debugger vs running the test suite. Tests tell you what fails; debug tells you why.

Understanding the topic

Core concepts to understand:

• Reproduce the failure with the exact input.
• Print tokens, full system + user + retrieved context.
• Test variations: remove sections one at a time (ablation).
• Try lower temperature to see if randomness is the cause.
• Try a stronger model to confirm prompt quality vs model capacity.

Syntax reference

Visual workflow / architecture:

Repro → Print full prompt → Ablate sections → Lower temp → Try bigger model → Pinpoint cause

Real-world use

LangSmith and OpenAI's playground let you inspect every prompt + response trace, then edit and rerun in place.

Best practices

• Always reproduce before guessing.
• Ablate sections to isolate cause.
• Treat debug findings as candidate eval cases.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. How debug a flaky prompt?
• Q2. What is ablation?
• Q3. How tell prompt vs model issue?
• Q4. How turn a debug finding into a regression?