Fine-Tuning
Adapting a pre-trained model to a specific domain or task by training on curated examples.
What is Fine-Tuning?
Fine-tuning adjusts model weights to improve performance on domain-specific tasks — legal language, medical terminology, internal process steps — without training from scratch. In enterprises, the decision between fine-tuning, RAG, and routing is an architecture choice with cost, latency, and data-handling implications. See Fine-Tuning vs Routing vs Smaller Models and Model Fine-Tuning.
Why it matters for on-premise & regulated AI
Fine-tuning embeds your data into model weights — once trained, that knowledge cannot be deleted from the model, only the model discarded. Doing it on-premises keeps training data, checkpoints, and the resulting weights under your retention and access policies, and sidesteps the contractual ambiguity of shipping regulated data to a provider’s training pipeline. For most teams, private fine-tuning of small open-weight models beats renting a fine-tuned frontier model.
Read the full guide: Fine-Tuning — in-depth article →
Related terms
Putting Fine-Tuning to work?
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