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Fine-tuning

Fine-tuning is the process of additional training of a pre-trained language model on a specific dataset to adapt it to a particular domain, task, or style. While the initial training of an LLM requires enormous computational resources and data, fine-tuning is much more efficient: you start from the base model and "specialize" its weights on targeted examples.

Definition

Through fine-tuning, you can create models that speak in a specific company's tone of voice, know the terminology of a vertical sector, automatically respect certain format or content rules, or avoid undesired behaviors.

Related terms

• LLM (Large Language Model)
• RAG (Retrieval Augmented Generation)
• Embedding
• Inference
• Prompt Engineering

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• RAG (Retrieval Augmented Generation)
• Embedding
• API

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