CiterLabsILLUSTRATIVE SOLUTION SCOPE
Model evaluation & fine-tuning feasibility
Find out whether model adaptation is justified before committing to a training experiment.
Make the task measurable
Define the input, expected output and failure modes. Classification, extraction and consistent formatting can be assessed with task-specific examples. Broad requests to “make the model smarter” do not provide a useful basis for evaluation.
Check the data first
Confirm rights to use the records, privacy requirements, label quality and whether the examples represent production conditions. Separate training material from evaluation material. Duplicates, inconsistent labels and leaked answers can make an experiment appear better than it is.
Establish alternatives
Test a prompting baseline and consider conventional software or retrieval where appropriate. Compare solutions against the same evaluation cases. Fine-tuning should be considered when the task, model support and available data make it a reasonable experiment.
Run a gated experiment
If feasibility checks pass, agree the model, dataset preparation, compute budget and evaluation plan. Review errors alongside aggregate measures. Document where the adapted model performs differently and where it still requires a human checkpoint. Gains are not guaranteed.
Deliverables to agree
Readiness assessment, data preparation plan, baseline results, evaluation rubric and, if viable, a training experiment with documented findings. The outcome may be a recommendation to improve data or use another approach rather than deploy a tuned model.
Questions for your project brief
- What exact task should improve?
- Are data rights and privacy requirements clear?
- Which baseline already exists?
- What errors are unacceptable in production?
START A CONVERSATION
Turn a business problem
into a clear project.
Bring a task, a bottleneck, or a product idea. The first step is a clear scope.