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Practice

What are the Benefits of Fine-tuning?

As discussed in the previous lesson, fine-tuning is the process of retraining a pre-trained AI model to adapt it for specialized tasks.

One of the key advantages of fine-tuning is the ability to develop an AI model that is customized for a specific domain while integrating the latest information.

But how can a fine-tuned AI model be practically applied in the workplace?


1. Creating AI Models Optimized for Specific Domains​

Fine-tuning an existing AI model with financial analysis data allows for enhanced performance in tasks such as stock market predictions, risk assessments, and financial report summarization.

Financial institutions benefit from these fine-tuned models by making faster and more precise data-driven decisions.


2. Improving Customer Service Chatbots with User Feedback​

By continuously fine-tuning customer service chatbots with customer interaction data, businesses can improve the quality of automated responses and enhance customer satisfaction.

Additionally, incorporating user language patterns and specific requests leads to more natural and engaging chatbot interactions.


To keep up with rapidly changing market trends, ongoing fine-tuning is necessary.

Fine-tuned AI models that incorporate real-time industry insights can provide dynamic market analysis, helping businesses stay competitive by responding to evolving customer interests and industry shifts.


4. Providing Personalized User Experiences through Recommendation Systems​

By fine-tuning an AI model using users' ₩service usage data₩, you can create a more personalized recommendation system.

For example, by considering a user's viewing history, you can recommend movies or TV shows tailored to their preferences.


Important Information Regarding Fine-tuning​

Due to OpenAI’s policies, external services are restricted from directly fine-tuning OpenAI models due to policy and technical limitations.

Fine-tuning requires user-uploaded training data, which may contain sensitive personal or confidential information, making it subject to strict privacy regulations.

Additionally, OpenAI imposes a limit of three simultaneous fine-tuning jobs on external platforms, which prevents CodeFriends from offering independent fine-tuning services.

The purpose of the comprehensive fine-tuning practice environment is to provide essential knowledge` for performing fine-tuning on the OpenAI platform and to guide users in creating JSONL datasets required for actual fine-tuning.


Practice​

In the practice environment on the right, compare the General GPT Model with the fine-tuned Grandma Model.