What you will learn
Prompting vs RAG vs tuning, Training data, LoRA, Held-out evaluation, Serving cost.
Engineering the capability
Different failure types call for different interventions. Missing current knowledge often calls for retrieval. An unclear instruction calls for a better task contract. Repeated style or task-format failures may justify supervised adaptation once simpler approaches have been evaluated.
Fine-tuning changes model parameters using training examples. Parameter-efficient approaches such as LoRA learn a smaller update instead of training every weight. They still require suitable data, evaluation, and a serving plan. Lower training cost does not eliminate the risk of overfitting or inappropriate behavior.
Prepare datasets around a precise task. Deduplicate related examples, remove sensitive data, document usage rights, and split by source or time when appropriate. Keep a baseline with the same inference conditions. Evaluate general behavior as well as the target task: a model that gains on narrow training-like examples may regress elsewhere.
Worked case
An assistant returns the wrong tone but usually finds the right policy. Start with instructions and representative examples. If tone failures persist across a labeled evaluation set, adaptation may be worth a trial. If it invents a price that changed yesterday, training on old examples is a poor substitute for a current authorized source.
Put it into practice
Continue with the next lab: prepare a model adaptation decision. Build the artifact, record the failure cases, and explain the tradeoff before moving on.