Fine-Tuning

Fine-tuning is the process of further training a pre-trained large language model on a smaller, specialized dataset to improve its performance on specific tasks or domains. It adjusts the model's internal weights so that it becomes better at the particular kind of work you need it to do.

How It Works

A pre-trained LLM has already learned general language understanding from a massive corpus. Fine-tuning takes this foundation and continues the training process using a curated dataset of examples that represent your target task -- such as customer support conversations, code in a specific framework, or medical terminology.

During fine-tuning, the model's parameters are updated (often only a subset, using techniques like LoRA or QLoRA) to minimize errors on the new dataset. The result is a model that retains its broad capabilities while becoming significantly more skilled at the specialized task.

Why It Matters

While prompt engineering and RAG can go a long way, some use cases benefit from a model that inherently understands domain-specific patterns, terminology, and style. Fine-tuning can improve output quality, reduce the need for lengthy system prompts, lower per-request token costs (by requiring less in-context guidance), and enforce consistent formatting or behavior that is difficult to achieve through prompting alone.

In Practice

Fine-tuning is most valuable when you have a well-defined task with sufficient training examples (typically hundreds to thousands) and when prompt engineering alone does not yield the desired consistency. For AI assistant deployments, fine-tuned models can better match your organization's coding conventions, communication style, or domain expertise. However, fine-tuning requires more upfront investment than prompt engineering and must be repeated when the base model is updated, so teams often start with RAG and prompting before considering fine-tuning.