What is Fine-tuning?
Turkish: Fine-tuning
Fine-tuning retrains a pre-trained model on selected examples so it behaves more consistently for a task, tone, or domain.
What is Fine-tuning?
Fine-tuning is additional training applied to a model that has already learned general patterns. The goal is not to create a model from scratch; it is to make behavior more consistent for a specific output format, classification rule, tone, or domain.
How Does It Work?
The process starts with high-quality training examples. For an LLM, those examples are often pairs of user input and desired response. During training, the model weights are updated in small steps, then performance is checked against a separate validation set.
In broader machine learning projects, fine-tuning is also used for image classification, text classification, and speech models. General knowledge learned during pre-training is retained while the model adapts to the target task.
When Is It Needed?
Fine-tuning is useful when prompting alone cannot deliver a consistent format, a specific labeling rule, or a high-volume repeated task. For internal knowledge search, teams should usually try RAG, better prompt design, and data cleanup before fine-tuning.
Risks include memorization, data leakage, learning incorrect behavior from bad examples, and judging success only by training scores. A fine-tuned model needs a clean dataset, evaluation cases, and production monitoring.
Related Terms
An LLM is a model trained on large text datasets that can understand and generate natural language, forming the basis of tools like ChatGPT.
LLMOpsLLMOps is the practice of testing, monitoring, versioning, and safely operating applications built on large language models.
Machine LearningMachine learning trains models on data patterns so software can make predictions, classifications, or decisions on new examples.
Token (LLM)A token is a word, subword, character, or symbol unit that language models process, shaping context size, cost, and speed.