Iterative prompt debugging in production

Prompts are software. Software has bugs. Production teams treat prompts with version control, evaluation, and rollback. This lesson teaches you to do the same.

A chef does not invent a new dish in front of paying customers. They prototype, taste, iterate, then ship. Prompt engineers do the same.

The iterative loop:

1. Define success: what does "good output" look like? Make it concrete.
2. Build an eval set: 20 to 100 representative inputs with expected outputs.
3. Write a baseline prompt.
4. Score outputs: automated (regex, schema) or LLM-as-judge.
5. Diagnose failures: cluster errors by type.
6. Patch one variable at a time.
7. Re-score.
8. Promote to production.

This is exactly how regular software is tested.

A lightweight eval in Python:

import json

cases = json.load(open("eval.json"))  # list of {input, expected}
score = 0
for c in cases:
    out = call_llm(c["input"])
    if matches(out, c["expected"]):
        score += 1
print(f"Score: {score}/{len(cases)}")

For more advanced evals: use Promptfoo, LangSmith, OpenAI Evals, or Helicone.