What is fine-tuning in the OpenAI API and when should you use it?

Fine-tuning creates a customised version of an OpenAI model trained on your own examples. It is used when prompting or few-shot examples are insufficient to achieve the desired style, format, or domain-specific accuracy.

from openai import OpenAI
client = OpenAI()

# 1. Prepare training data (JSONL format)
# Each line: {"messages": [{"role": "system", "content": "..."}, ...]}
# Save as training.jsonl

# 2. Upload training file
training_file = client.files.create(
    file=open("training.jsonl", "rb"),
    purpose="fine-tune"
)

# 3. Create fine-tuning job
job = client.fine_tuning.jobs.create(
    training_file=training_file.id,
    model="gpt-4.1-mini-2025-04-14",   # supported base models
    # model="gpt-4.1-2025-04-14",
    hyperparameters={
        "n_epochs": 3,
    }
)

# 4. Monitor job
job_status = client.fine_tuning.jobs.retrieve(job.id)
print(f"Status: {job_status.status}")

# 5. Use fine-tuned model
response = client.chat.completions.create(
    model=job_status.fine_tuned_model,  # e.g. ft:gpt-4.1-mini:my-org::abc123
    messages=[{"role": "user", "content": "..."}],
)

When NOT to fine-tune first: prompt engineering, few-shot examples, and RAG (retrieval-augmented generation) should be tried before fine-tuning. Fine-tuning requires training data, incurs training costs, and has longer iteration cycles. Reserve it for cases where the base model consistently fails despite good prompting.

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