AI Glossarytechniques

Fine-Tuning

The process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.

How It Works

Fine-tuning takes a general-purpose model and specializes it. You provide training examples (input-output pairs) and the model adjusts its weights to better handle your use case. OpenAI supports fine-tuning GPT-4.1-mini with as few as 10 examples. However, fine-tuning is often unnecessary: RAG and prompt engineering solve 90% of customization needs at lower cost and effort. Fine-tune when you need consistent formatting, domain-specific tone, or performance on a narrow task.

Common Use Cases

  • 1Custom writing styles
  • 2Domain-specific classification
  • 3Consistent output formatting
  • 4Specialized code generation

Related Terms

Large Language Model (LLM)

A neural network trained on massive text datasets that can generate, understand, and reason about human language.

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.

Prompt Engineering

The practice of crafting effective instructions for AI models to produce desired outputs consistently.

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