Dataset preparation
Review data suitability, usage rights and labeling quality. Separate training and evaluation material to reduce misleading results.
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Adapt model behavior to a specific domain or workflow. Shape the training data, establish an evaluation baseline, and compare changes against the task that matters.
Discuss your projectFor tasks where a general model's behavior needs closer alignment with your domain or format. Establish a baseline and evaluation set first, then determine whether prompting, retrieval or training is the appropriate intervention.
Training examples and held-out evaluation tasks have different roles. Adapt the model, measure it against the baseline and use the findings to decide the next iteration.
Separate training examples from evaluation tasks.
Review data suitability, usage rights and labeling quality. Separate training and evaluation material to reduce misleading results.
Select an adaptation approach for the task and available resources. Track the model configuration and training data used for each iteration.
Compare behavior on representative held-out tasks, including failure cases. Assess quality alongside latency and operating cost.
A useful first conversation starts with the task, the systems involved and what a successful result would look like.
01 What does the current model get wrong on representative examples?
02 What training material can you use, and how has it been labeled?
03 What evaluation criteria and deployment constraints should guide the work?
A new product, a complex integration, or a workflow ready for change. Tell us what you have in mind.
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