Meaning
The process of taking a pre-trained base AI model and further training it on a specific, domain-targeted dataset to specialize its behavior.
Three examples from work
Strategy debate
CTOWhen should we consider fine-tuning over in-context learning?
AI LeadWhen we need strict adherence to custom syntax and lower latency per request.
Data preparation
Data EngineerWe curated ten thousand high-quality conversation pairs for fine-tuning.
DevEnsure all personally identifiable information is sanitized first.
Evaluation
LeadDid the fine-tuning improve our code translation accuracy?
DevYes, syntax error rates dropped by forty percent compared to the base model.
Fine-tuning modifies model weights permanently, while RAG provides dynamic context at runtime. Use fine-tuning to teach form and style, not volatile facts.