What is Fine-Tuning?

Fine-tuning is the process of adapting a pre-trained language model to perform a specific task with greater accuracy on domain-specific inputs. It enables general models like GPT to become highly effective in domains such as medical summarization and customer service automation. 

The approach is a type of transfer learning, where an existing model is updated to perform a new but related task. Transfer learning builds on prior training to reduce the need for retraining a model from scratch. Fine-tuning builds on this approach by adapting the model with smaller amounts of task-specific data while improving its performance.

The model is fed new examples during a short training phase, during which it adjusts internal parameters — the weights that influence predictions — without losing general language capabilities. The result is a model tailored to specific, high-impact business tasks such as summarization or retrieval.

How does fine-tuning work?

Getting started with the fine-tuning process can be complex, so it is useful to begin with a clear overview of the typical steps involved.

Below are the typical steps involved in fine-tuning a model for use in specific tasks.

Selecting a pre-trained model

Fine-tuning begins with a model already trained on large-scale datasets. Pre-trained models are available on platforms like Hugging Face and are grouped by data type, such as text or images.

Selection depends on the model’s architecture, its training data, and the input format it accepts. Teams assess which model best suits the task and tech stack, choosing between open-source LLMs or commercial closed-source LLMs.

Preparing and formatting the data

The model is then provided with labeled examples that demonstrate the task it is expected to learn. Data is cleaned, removing inconsistencies, duplicates, or formatting errors. It is then split into training and validation sets. 

Training examples must be formatted to match the model’s required input-output structure. Model cards often specify an instruction format. JSONL is a common structure, with each line representing a training pair.

Setting fine-tuning parameters

Data scientists decide how the model will learn from the training data. The learning rate controls how much the model updates its understanding after each example. The batch size sets how many examples are processed at once, influencing speed and learning quality. The number of epochs refers to the number of times the model will loop over the entire dataset.

These parameters must be balanced carefully. If they are set too low, the model may fail to learn enough (underfitting); if they are too high, the model may learn the training data too precisely and perform poorly on new data (overfitting). Often, earlier layers are frozen — meaning they are not updated during training — to preserve general knowledge, while later layers are fine-tuned to the specific task.

Running the fine-tuning process

Once the setup is complete, the fine-tuning begins. It will usually take place on a cloud platform that can handle large-scale processing. The model processes the training data in batches, gradually adjusting its internal parameters to better perform the task based on the examples provided.

The platform often provides progress updates, such as how many training steps have been completed. When the process finishes, a new version of the model is saved with its own unique ID. It retains general language capabilities but is now adapted to handle the specific task with greater accuracy.

Evaluating model performance

After training, the model is evaluated using the validation dataset to assess how well it performs on new, unseen examples. 

Accuracy measures how often the model provides a correct response. Precision is the proportion of the model’s positive predictions that are correct, while recall indicates how many of the total correct answers the model successfully identifies. The F1 score combines precision and recall to provide a balanced measure of performance.

To improve results, teams often repeat the fine-tuning process with cleaner data or adjusted settings.

Deploying the fine-tuned model

Once the model has achieved satisfactory results during evaluation, it is deployed to a server or cloud environment where it can be accessed by applications in real time. The model uses the knowledge it gained during fine-tuning to make predictions or generate outputs when new input is received.

After deployment, teams continue to monitor how the model performs in real-world conditions. Over time, changes may occur in how users behave, the types of data being processed, or the tasks the model is expected to complete. These shifts can affect the model’s accuracy. If performance degrades over time, the model can be fine-tuned again with updated examples to ensure it continues to meet system requirements.

Fine-tuning vs. prompt engineering vs. Retrieval-Augmented Generation (RAG)

Fine-tuning is often confused with other approaches such as prompt engineering and Retrieval-Augmented Generation (RAG). While all three improve the performance of large language models (LLMs), they do so using different methods.

Each technique has strengths and limitations, depending on the task requirements, need for real-time information, and available technical resources.

The table below compares the three approaches.