What Are the Benefits of Fine-Tuning?
As we explored in previous lessons, fine-tuning refers to the process of retraining a pre-trained model to specialize in a specific task.
The primary advantage of fine-tuning is the ability to create an AI model that is tailored to a specific domain with up-to-date information.
So, how can you apply a fine-tuned AI model in real-world scenarios?
Here are some of the major benefits you can gain from fine-tuning.
1. Creating AI Models Optimized for Specific Domains
When an existing AI model is fine-tuned with financial analysis data, it can become highly specialized for tasks such as stock market predictions, risk analysis, and summarizing financial reports.
These fine-tuned models assist financial institutions in making faster and more accurate decisions.
2. Enhancing Customer Service Chatbots with User Feedback
By continuously fine-tuning a customer service chatbot with a company's own customer interaction data, you can increase customer satisfaction by providing responses tailored to your specific services.
Furthermore, it allows you to implement more natural conversations by incorporating customers' language patterns and specific requirements.
3. Reflecting Latest Information and Trends in Market Trend Analysis
To incorporate rapidly changing market trends into an AI model, ongoing fine-tuning is essential.
With a fine-tuned model that reflects the latest information, you can periodically analyze market data and integrate customers' current interests into your business.
4. Customized User Experience Through Recommendation Systems
By fine-tuning an AI model based on users' service usage data, you can build a highly personalized recommendation system.
For instance, you can offer tailored movie or TV show recommendations based on a user's viewing history.
Important Notes on Fine-Tuning
Due to OpenAI's policy, external services are restricted from connecting to OpenAI for fine-tuning due to policy/technical issues.
This is because the training data uploaded by users for fine-tuning may contain sensitive personal and confidential information.
Additionally, OpenAI restricts the number of simultaneous fine-tuning tasks to a maximum of 3 on external platforms, which is why CodeFriends cannot provide a separate fine-tuning service.
The goal of the comprehensive fine-tuning practice environment you're about to experience is to provide the essential knowledge required to perform actual fine-tuning on the OpenAI platform, and to assist in building the necessary JSONL dataset for the actual fine-tuning process.
Practice
In the practice environment on the right, compare the General GPT Model with the fine-tuned Southern Grandma Model.
Want to learn more?
Join CodeFriends Plus membership or enroll in a course to start your journey.