Customizing AI models with private data is a powerful way to enhance their performance for specific tasks. You can tailor the model to generate context-relevant content by training it with information such as industry-specific terminology or company-specific jargon.

This customization can lead to more accurate and personalized responses, predictions, or forecasts.

How they work in the real world

Techniques like fine-tuning, retraining, or Retrieval-Augmented Generation (RAG) open up various practical applications.

For instance, in customer service, a company can retrain an AI model to better understand and respond to user queries. In the stock trading industry, where data is dynamic and real-time, regular fine-tuning of the model improves its accuracy with the latest data.

With RAG, businesses can enhance search and information retrieval systems, ensuring more relevant and accurate results. These techniques empower organizations to adapt AI models to specific tasks, making them versatile tools across different industries and sectors.

Ensuring the security of private data when using it to train AI models is crucial, yet still in an exploratory phase.

Recent news about OpenAI models leaking private data have diverted the focus on security, such as where the models are being hosted, how the models are fine-tuned, etc. One way to ensure your data is secure is to host the model locally, as offered by Mistral AI. With MindsDB you can fine-tune such a model with just a few commands, check out this tutorial.

Understanding the concepts

You can use a number of methods to customize AI models using private data. Let's explore three of the most notable techniques.

Fine-tuning

Fine-tuning is a process of making adjustments to a pre-trained model using a new set of data. Imagine you have a customer support chatbot that uses a pre-trained large language model (LLM), like OpenAI, or Anthropic.

This chatbot can answer customers' inquiries. However, it doesn't know the company's internal policies required to provide comprehensive responses. That's where fine-tuning can help. You can fine-tune the model with private data to give it relevant context for providing answers to customers.

The above diagram illustrates how fine-tuning works. It takes a pre-trained model and a new (and potentially private) dataset as input. And its output is this pre-trained model enhanced with additional knowledge.

Retraining

Retraining is a process of (re)training an AI model from scratch.

The retraining process of an AI model can be thought of as a completely new training run, with an updated input dataset (any original dataset plus relevant new data). Compared to fine-tuning, retraining the model with all data at once usually leads to improved core model abilities, while still learning relevant facts that any portion of the data may contain. This helps the model stay up-to-date and improve its abilities, specifically when there are new things to learn or changes in the information it deals with.

The above diagram illustrates how retraining works. It takes an AI model, the original data it was trained with, and new data. It trains the model from scratch using both original and new/updated data.

RAG

Retrieval-Augmented Generation (RAG) approach introduces an additional layer where artificial intelligence dynamically retrieves information from external or internal data sources in real time.

Follow our CTO’s Guide to RAG to learn more about the benefits and use cases of this technique.

Customize LLMs with MindsDB

MindsDB makes it easy to customize AI models using different methods. You can enhance AI models by fine-tuning them, including LLMs from OpenAI or Hugging Face, as well as AutoML models. It also lets you retrain AutoML models, including ones that handle time-series data.

And, if you want to use Retrieval-Augmented Generation (RAG) in your AI-powered apps, MindsDB has a special RAG integration that's ready to use in minutes.

Moreover, MindsDB facilitates continuous customization of AI models using dynamic and real-time data, which is accomplished with the Jobs feature. In real-world use cases, we usually deal with real-time and dynamic data that changes every day, every hour, or even every minute.

Hence the need for fine-tuning or retraining AI models with the new-coming data to keep them up-to-date and as accurate as possible.

• MindsDB provides the FINETUNE command that allows for fine-tuning of LLMs, including OpenAI, Mistral, and more, as well as AutoML models, like classification or regression models. Check out this article that presents a comprehensive tutorial on fine-tuning the Mistral model.

• MindsDB provides the RETRAIN command that allows for the retraining of AutoML models; it works analogically to the FINETUNE command except for the fact that it uses the entire original dataset plus the new dataset to retrain the model (as explained in the concepts chapter above). Check out this article that presents a comprehensive tutorial on retraining a time-series model to enhance its accuracy as new data becomes available.

• MindsDB provides the RAG integration used to create RAG-based models, that can be fed with data from databases, files, and websites, to answer questions. Follow our CTO’s Guide to RAG to learn more about the benefits and use cases of this technique.

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