Fine Tuning And Customization

Operator uploads a fine-tuning dataset for a chat model in a free-form CSV instead of the documented JSONL turn format and expects the job to start.

Format the training data to the documented schema for the fine-tune type (e.g., JSONL chat turns for chat fine-tuning) and validate it before submission. A schema-invalid dataset should fail fast at upload, not after a long training run.

A fine-tune job is submitted; the integrator assumes it is ready immediately and routes production traffic to a not-yet-finished model.

Poll the fine-tune job to a terminal status (succeeded/failed) before routing traffic; handle the failed terminal state by inspecting the error rather than retrying blindly. Do not assume synchronous completion.

A fine-tuning dataset contains customer PII that the operator uploads without redaction or a data-handling review.

Treat training data as sensitive: minimize and redact PII before upload, confirm the data-handling/retention terms for fine-tuning, and keep an audit record of what was uploaded. Confirm retention and deletion terms [REQUIRES-VERIFICATION] before submitting regulated data.