AI Definition

Fine-tuning

Continuing to train an existing model on your own data so it specializes for your task.

Fine-tuning takes a pretrained foundation model and continues its training on a smaller, task-specific dataset. The result is a model that's better at your specific use case but it is no longer the same model the original provider ships.

In 2026 most teams use fine-tuning sparingly. RAG is usually a better first move for adding knowledge, and good prompt engineering plus tool use covers a lot of ground. Fine-tuning shines when you need a specific writing style, structured output, or to make a smaller (cheaper) model behave like a larger one for a narrow task.

LoRA and QLoRA fine-tuning have made the process much cheaper you train low-rank adapters instead of every weight. Tools like Hugging Face's PEFT, Modal, and Together AI have made fine-tuning accessible to small teams.

Tools related to Fine-tuning

Together AI

Run, fine-tune, and serve 200+ open-source models.

Replicate

Run open-source models in the cloud with a one-line API call.

Related concepts

LLM (Large Language Model)

A neural network trained on huge amounts of text to predict and generate language.

LoRA (Low-Rank Adaptation)

A cheap fine-tuning technique that trains small adapter weights instead of the entire model.

RAG (Retrieval-Augmented Generation)

Retrieving relevant documents at query time so a language model can answer with grounded, up-to-date information.

Prompt Engineering

The discipline of writing inputs to language models that reliably produce useful, structured outputs.

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