Glossary
Fine-tuning
Definition
Fine-tuning is further training of an existing model on a smaller, task-specific dataset so it performs better on that task or follows a particular style or format.
Why it matters when you’re buying
It can raise quality and lower inference cost for narrow, repeated tasks, but it needs good training data, evaluation and a plan to repeat the work when the base model changes. For knowledge that changes often, RAG is usually the better fit.
How it relates to other terms
Fine-tuning changes the model itself, while RAG leaves the model alone and changes what it reads. MLOps practices keep tuned models versioned and traceable.
Where to go next
- AI & ML Platforms: news, companies and community questions
- How to evaluate AI & ML Platforms vendors: criteria, questions to ask and red flags
- MLOps and inference: the latest stories and threads on this topic
Written by Techarda editors · Updated . Think a definition is wrong or missing something? Email engage@techarda.com. All terms.