AI Prompt Engineering · Lesson

Systematic Debugging Approach

Binary search on prompt sections: remove half, test, narrow down the issue.

The Debugging Mindset

Prompt debugging mirrors software debugging: do not change multiple things at once, do not guess randomly, and do not deploy a fix you cannot explain. A systematic approach uses binary search logic — narrow the problem space by half with each test — to find the minimal failing case efficiently.

Step 1: Reproduce the Failure

Before debugging, reliably reproduce the failure. A failure you cannot reproduce consistently cannot be debugged systematically.

Run the prompt 5 times on the same input. If it fails every time: deterministic failure — easy to debug. If it fails sometimes: probabilistic failure — set temperature=0 first to eliminate randomness, then re-test.

import openai
client = openai.OpenAI(api_key='sk-...')

def run_prompt(prompt, user_input, temperature=0):
    resp = client.chat.completions.create(
        model='gpt-4o',
        messages=[
            {'role': 'system', 'content': prompt},
            {'role': 'user', 'content': user_input}
        ],
        temperature=temperature
    )
    return resp.choices[0].message.content

# Reproduce with temperature=0 to eliminate randomness
for i in range(5):
    output = run_prompt(failing_prompt, test_input, temperature=0)
    print(f'Run {i+1}:', output[:100])

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