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Fine-tuning adapts a pretrained language model to examples of the behaviour you want. Use it for text responses, structured answers or tool calls. Your coding agent can prepare the examples, train the model and compare its outputs with the original base model in one workflow. This walkthrough uses the same ecommerce fraud dataset as the decision-model tutorial. Here, the model generates a text label: fraud or not_fraud. The decision-model walkthrough covers typed probability outputs.

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Before you begin

Connect your coding agent and select a separate tutorial project. You do not need an application repository or a previously trained decision model. This workflow includes training and benchmark inference, which can incur provider costs. Your agent explains the proposed model and expected cost before launch. The agent carries the workflow through preparation, training and benchmarking, bringing back decisions that need your input. You do not need a separate request for each stage.

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What happens next

  1. Prepare examples. The agent turns transaction details into user messages and the supplied labels into assistant responses. It keeps is_fraud out of the input, retains the preparation, and separates training, development and held-out data.
  2. Define the comparison. It prepares held-out inputs with separate reference labels and proposes checks for valid output labels and classification correctness. You review the evaluation criteria before they are used.
  3. Fine-tune a model. It selects a compatible language model, checks the training setup and expected cost, and follows the accepted run through development validation.
  4. Benchmark the result. It compares generated responses from the base and fine-tuned models on the same held-out examples using the same checks.
The agent prepares chat-training examples for this path. See conversation and evaluation formats if you want to inspect their structure. Development checks help monitor training; the final held-out comparison measures the result separately.

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Check the result

At the end, the agent should show:
  • The completed training run, selected base model and exact data versions used.
  • Base-model and fine-tuned responses alongside the held-out reference labels.
  • Classification results, including missed fraud and legitimate transactions flagged as fraud.
  • Invalid responses, missing results and the number of examples scored for each model.
  • Whether the fine-tuned model improved on the base model, with representative mistakes and limitations.
A response can use the correct label format and still be wrong. The source also contains many more non-fraud transactions than fraud transactions, so overall accuracy alone is insufficient. The dataset reference records its label counts and limitations. These are synthetic examples. A better result here does not establish performance on real transactions, and fine-tuning may not improve on the base model.

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Continue in the same conversation

Use Monitor training to inspect the run, Run an evaluation for another comparison, or Serve a model to call a ready deployment from your application.