When Would Fine-Tuning Improve MailTrace?

Identify the error you want training to change

A useful candidate might be a repeated failure to follow a well-defined classification policy despite adequate context. Missing document text belongs to extraction. An omitted relevant passage belongs to retrieval. A foreign source ID belongs to deterministic validation. Treating all four as model problems creates an expensive and poorly targeted experiment.

The supplied records do not establish that a fine-tuned model is deployed in MailTrace. A proposed experiment would need reviewed examples, permission to use them, a clear target task and an untouched evaluation set. Sensitive case material should not become training data simply because it already exists in the application.

Write the adoption gate before running training

This example is a proposed decision record. Its criteria are deliberately qualitative until the team sets measurable thresholds from a labelled baseline. No training job or provider call is made by this article.

{
  "task": "classify a bounded email review proposal",
  "baseline": "current prompt and model",
  "required": ["better held-out task quality", "no case-isolation regression"],
  "track": ["abstention", "reviewer corrections", "latency", "cost"],
  "status": "experiment_not_run"
}

Keep retrieval and validation after training

Even a successful fine-tune still needs current case context, ownership checks, source freshness and citation validation. Training should not make the model the store of a user’s facts. New evidence must remain available through the retrieval and provenance workflow.

Compare against improvements to prompts, context selection and a smaller qualified model before accepting additional training and maintenance cost. If a fine-tune is adopted, record its training dataset version and maintain rollback to the previous configuration. A useful result is a measured improvement in a bounded task, not the prestige of owning a customised model.