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Plain-language AI glossary

Term 07TechniquesMeaning / context / connections

Techniques / Definition

Fine-Tuning

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

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01

MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

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UsesWhere the concept becomes useful.

01 / Plain-language context

How Fine-Tuning works.

Fine-tuning takes a general-purpose model and specializes it using examples of the behavior you want. The model adjusts its weights so it can reproduce patterns that are difficult to express through instructions alone. Before fine-tuning, test whether clearer prompts, retrieval, structured outputs, or better examples solve the problem. Fine-tuning is most useful when you need repeatable behavior on a narrow task and have a representative evaluation set.

02 / Practical uses

Where it helps.

  1. 01Custom writing styles
  2. 02Domain-specific classification
  3. 03Consistent output formatting
  4. 04Specialized code generation
03 / Keep exploring
RAG (Retrieval-Augmented Generation)A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.Prompt EngineeringThe practice of crafting effective instructions for AI models to produce desired outputs consistently.Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.