Comparison
Fine-tuning vs Large Language Model
Fine-tuning and Large Language Model are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Fine-tuning
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples.
Fine-tuning Llama 3 on medical Q&A for a clinical assistant.
When you would reach for Large Language Model
Large Language Model comes up when the question is fundamentally about foundations.
Claude Sonnet — Anthropic's general-purpose LLM.
Frequently asked
What is the difference between Fine-tuning and Large Language Model?
Fine-tuning: Fine-tuning continues training a pretrained model on a smaller, task-specific dataset, adjusting its weights to specialize behavior or knowledge. Large Language Model: A large language model is a neural network trained on huge amounts of text to predict the next token in a sequence. GPT-4, Claude, and Gemini are all LLMs.
When should I use Fine-tuning vs Large Language Model?
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples. Large Language Model applies when you are focused on foundations.
Are Fine-tuning and Large Language Model the same thing?
No. Fine-tuning is training; Large Language Model is foundations. They are related but address different parts of the AI stack.