Fine-Tuning
The process of further training a pre-trained AI model on specific data to specialize it.
Fine-Tuning is the process of taking a pre-trained AI model (like a large language model) and continuing to train it on specific data to adapt it for a particular task or domain. Fine-tuning can dramatically improve performance on specialized tasks but requires technical expertise and quality training data.
Most production LLM applications today rely on prompt engineering and RAG rather than fine-tuning — both are typically faster, cheaper, and more flexible. Fine-tuning becomes valuable for high-volume specialized use cases or for replicating very specific style or behavior.
A legal tech company fine-tunes a smaller open-source model on their corpus of legal documents. The fine-tuned model becomes specialized at legal reasoning at a fraction of the cost of using a frontier model.
Related terms
A neural network trained on vast text data to understand and generate human language.
A technique that combines LLMs with retrieval of external information to ground responses in facts.
The practice of crafting effective inputs to AI models to produce desired outputs.
Need help applying Fine-Tuning to your business?
Book a free 30-minute strategy call. I'll show you how Fine-Tuning fits into a real growth strategy for your business.
Book a free strategy call