Fine-tuning
Taking a general AI model and training it further on specific examples to make it better at a particular job. Like hiring an experienced generalist and then giving them months of specialized training in, say, legal contracts or medical billing. The model keeps its general abilities but gets noticeably better at the thing you trained it on. Relevant if you're building a custom AI product — not something everyday Claude users need to worry about.
In practice
You run a medical billing company and Claude's general-purpose writing style isn't precise enough for your coding outputs. You fine-tune it on 10,000 examples of correct billing code explanations. Now your custom model speaks in the exact format and terminology your team expects — it's been specialized beyond what prompting alone can achieve.
Related concepts
Where Fine-tuning shows up
2 articlesFine-tuning is how you train a model on your specific data to change its behavior at a deeper level than prompting can reach. It's powerful — and often unnecessary. Knowing which situation you're in saves a lot of time.
The question most AI founders ask in month two. The honest answer covers fine-tuning economics, the cases where Claude is genuinely insufficient, and the trap of premature optimization.