What is Fine-tuning?

Fine-tuning is a process in which an existing, pre-trained AI model is further trained on a specific dataset to better perform specific tasks or adapt to a specific communication style. It’s like taking a talented, well-educated employee and providing them with additional, specialized training tailored to your company’s needs.

How Does Fine-tuning Work in Practice?

Fine-tuning is the technical name for the process of “retraining” an already existing AI model. To understand this, imagine an AI model as a huge neural network consisting of billions of connections, each with an assigned weight (parameter) determining its strength. These weights have already been “learned” during the initial training on huge collections of internet data.

During fine-tuning, we don't start from scratch - instead, we take this already trained model and show it hundreds or thousands of carefully selected examples of how we want it to behave. Each example typically consists of:

• Input: questions, commands, or situations.
• Expected output: the ideal answer or response.

For example, if we're fine-tuning a model for customer service, one example might look like this:

• Input: “My ordered product doesn’t work, what should I do?”
• Output: “I’m very sorry for the problems with your product. To help you as quickly as possible, can you provide your order number? In the meantime, I’ll check available repair or replacement options.”

During the fine-tuning process, the algorithm compares the responses generated by the model with the ideal responses from the examples. Each time the model responds differently than the “exemplary” response, the algorithm gently adjusts the weights in the neural network so that future responses are closer to the desired ones.

This process is repeated thousands of times for all examples in the training dataset. Gradually, the model “learns” to recognize patterns in the data and adjusts its responses to the expected style, tone, and content. What’s key here is that the model doesn’t simply memorize the examples – it learns abstract rules and patterns that it can then apply to new, previously unseen situations.

The result is a model that retains its general knowledge and language abilities from the original training but gains specialized knowledge and a characteristic response style in a specific field.

Advantages of Fine-tuning

• Specialization: The model becomes an expert in a narrow field or specific task.
• Consistent style: The fine-tuned model consistently maintains a specific tone, format, and approach to answers.
• Performance: Often allows for shorter prompts because the model “understands” expectations without detailed instructions.
• Better quality: In its specialization, a fine-tuned model usually achieves better results than a general-purpose model.
• Cost-effectiveness in the long term: Despite initial investment, over time it can reduce costs through shorter prompts and better answers.

Disadvantages and Limitations

• Requires training data: You need many (usually hundreds or thousands) of high-quality examples for the model to learn.
• Technical complexity: The process requires more technical knowledge than other techniques (RAG, CAG).
• Initial cost: Fine-tuning is computationally and financially expensive.
• Risk of overfitting: The model can become too specialized and lose the ability to handle questions outside the scope of training.
• Difficulty updating: Updating the knowledge of a fine-tuned model requires retraining.
• Limited flexibility: The model may be difficult to adapt to new tasks without retuning.

Costs and Requirements

• Memory: The fine-tuned model itself requires similar memory as the base model, but the tuning process requires significant resources:

  • Storage of training data (from a few hundred MB to several GB)
  • Temporary model copies during the training process (tens or hundreds of GB)

• Infrastructure:

  • Powerful graphics cards (GPU) or specialized processors (TPU) to conduct the fine-tuning process
  • Systems for storing and managing training data
  • Tools for evaluating model quality

• Financial Costs:

  • One-time computational costs of fine-tuning (from several hundred to tens of thousands of zlotys, depending on model size)
  • Costs of preparing training data (often requires specialist work)
  • Hosting costs for the fine-tuned model
  • Costs of potential updates and retraining

Limitations Related to LLM Models

• Risk of forgetting general knowledge in favor of specialization
• Difficulty adding new knowledge without retraining
• Possibility of inheriting biases and errors from training data
• Limited ability to go beyond the scope of training data

Practical Examples of Application

Example 1: Medical Assistant

A hospital fine-tuned an AI model based on thousands of examples of answers to medical questions, in accordance with the latest guidelines and hospital standards. The fine-tuned model helps doctors interpret test results, suggests possible diagnoses, and reminds them of procedures using the terminology and approach adopted in that facility.

Example 2: Customer Service Assistant

An e-commerce company fine-tuned an AI model based on thousands of example customer interactions that were rated as exemplary. The model learned to answer product questions in the brand’s style, resolve typical customer issues, and direct more complex cases to appropriate departments.

Example 3: Marketing Content Generator

A marketing agency fine-tuned an AI model using examples of successful advertising campaigns from the past. The model learned to create slogans, product descriptions, and social media posts in a style consistent with company guidelines, maintaining a consistent brand message.

Security and Risk Control

Fine-tuning AI models involves important security issues that require special attention before implementation in a production environment. The fine-tuning process can unintentionally introduce or strengthen harmful behavior in the model, especially when training data contains inappropriate content, biases, or incorrect information.

A key risk is the possibility of “poisoning” the model through inappropriate training data, which can lead to generating false, biased, or potentially harmful responses. The model can also learn to disclose confidential information if such data is present in the training data. Therefore, before starting the fine-tuning process, it is essential to conduct a thorough audit of all training data, implement content filtering mechanisms, and establish procedures for testing the security of the fine-tuned model.

Additionally, organizations must consider issues related to personal data protection, especially when fine-tuning on data containing information about customers or employees. It is also important to establish mechanisms for monitoring the model’s behavior after deployment and procedures for quick response in case of detecting problematic responses.

Comparison Fine-tuning vs Retrieval Augmented Generation (RAG)

Aspect	Fine-tuning	Retrieval Augmented Generation (RAG)
Operation method	Modifies model parameters through additional training	Enriches prompts with information retrieved from external sources
Initial cost	Very high (thousands-tens of thousands of zlotys)	Relatively low (hundreds-thousands of zlotys)
Implementation time	Long (weeks-months)	Short (days-weeks)
Technical requirements	High (GPU/TPU, ML expertise)	Average (database, API)
Knowledge update	Requires retraining	Instant through database update
Quality in specialization	Very high	High
Flexibility	Low	Very high
Style consistency	Perfect	Good (depends on prompts)
Scalability	Limited	Very high
Transparency	Low (hard to understand why model responds this way)	High (can see information sources)
Hallucination risk	Medium in specialization, high outside it	Low (answers based on facts)
Best use case	Consistent style, specialized tasks, long-term projects	Dynamic knowledge, FAQ, information retrieval

Summary

Fine-tuning is the most advanced and potentially most effective technique for adapting AI models to specific needs, but also the most demanding in terms of resources and technical knowledge. It is an investment that pays off for repetitive tasks where consistency and specialization are key.