Build a baseline before choosing training
For document classification, first define the categories and test a simple baseline on representative examples. For a language assistant, compare clear instructions and relevant reference material before committing to fine-tuning. Write down what counts as success and what kinds of mistakes are unacceptable. This keeps the project focused on an outcome rather than a technique.
Match the approach to the gap
Fine-tuning continues training a pretrained model on task-specific data. It can be useful when you have consistent examples of the behaviour you want. Training a separate ML model may suit a well-defined prediction or classification problem. If an assistant mainly needs current company information, a retrieval workflow may be a better starting point than expecting training to keep facts current.
Keep evaluation data separate
Remove duplicates and split related records carefully. If examples from the same customer or event appear in both training and evaluation, the apparent result can be misleading. Record dataset versions and label definitions. Assess errors across relevant groups and situations, not only one overall score. Use only data you have the right to process.
Include the cost of running the model
Measure response time, resource requirements and behaviour when confidence is low. Plan a fallback and a way to review failures after deployment. Devoholic provides dataset preparation, ML model training, custom AI integration and model fine-tuning. We can help choose an approach, evaluate it against a baseline and integrate it into a usable product; a demo is not evidence of production accuracy.
Before you begin
- A specific task and a baseline
- Usable data, labels and processing rights
- A separate evaluation set and error analysis
- Latency, operating cost, fallback and monitoring
Bring the problem and the data you already have. We can identify whether training, fine-tuning or a smaller integration is the right next step.
Technical references
Hugging Face · Fine-tuningDevoholic studio · AI-assisted technical guide. Examples illustrate engineering decisions; they are not client performance claims.