What is fine-tuning?
A simple explanation of fine-tuning and when it is used instead of just prompting a model.
In simple terms
Fine-tuning means taking an existing AI model and training it further so it performs better on a specific kind of task.
Instead of making a model from scratch, you start with one that already knows a lot.
Then you train it further with more focused examples.
That helps the model behave better for a narrow kind of job.
It is usually used when prompting alone is not enough.
Why it matters
Fine-tuning matters when companies want more consistent behavior, formatting, or specialization from AI models.
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What is RAG?
A simple explanation of retrieval-augmented generation and why AI tools use it.
What is an LLM?
A simple explanation of what a large language model is and why it powers tools like ChatGPT.
RAG vs fine-tuning
A plain-English comparison of RAG and fine-tuning so people can understand when each one is used.
Common questions
What is fine-tuning in simple terms?
Fine-tuning means taking an existing AI model and training it further so it performs better on a specific kind of task.
Why does fine-tuning matter in AI?
Fine-tuning matters when companies want more consistent behavior, formatting, or specialization from AI models.
What should I read after learning about fine-tuning?
The best next step is to continue with related explainers, browse the category page, or follow the beginner path to keep learning AI step by step.