How to Fine-Tune GPT-4o-mini on Your Own Guardrail Failures (50 Lines of Python)

How to Fine-Tune GPT-4o-mini on Your Own Guardrail Failures (50 Lines of Python) Every time your LLM gets corrected by a guardrail, a training example is born and immediately thrown away. This tutorial shows you how to catch those examples and use them to make your model better — automatically, with no manual labeling. By the end, you'll have a working pipeline that: Validates LLM outputs against natural language requirements Retries failures with structured feedback Captures every (rejected → corrected) pair to disk Exports those pairs in OpenAI fine-tuning format Uploads to OpenAI for fine-tuning Total code: ~50 lines. Total manual labeling: zero. Prerequisites pip install "semantix-ai[all]" openai You'll need an OpenAI API key for the LLM calls and fine-tuning upload. The validation itself runs locally — no API cost. Step 1: Define What "Correct" Means Semantix uses Intent classes. The docstring is the requirement. That's it. from semantix import Intent class ProfessionalDecline ( I