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Beyond GPT: Custom Trained AI Models

Understanding AI Models

In the past, constructing a text generation model from scratch required extensive training over several months. However, the landscape of AI has evolved significantly. Today, we have pre-trained foundation models that excel at various language-related tasks. These models serve as a solid base for further improvement through a process known as fine-tuning.

GPT (Generative Pre-trained Transformer) models are pre-trained with information from the entire web, which is excellent for general-purpose chatbots like ChatGPT. These models do a good job of answering questions when the answer is somehow implicit in the training dataset. Essentially, they have learned from vast portions of internet content up until 2021.

The Role of Fine-Tuning

The key concepts in understanding AI models are the “Foundation/Base Model” and “Fine-tuned model.” With a pre-trained foundation model, it is possible to enhance its capabilities by fine-tuning it on a smaller dataset, refining its performance for a specific task.

The problem arises when the model (designed to generate content) doesn’t know the answer it needs to generate. This is when we encounter so-called “hallucinations.” It occurs with highly specific industry or company information. The model invents an answer because its primary function is content generation, not providing accurate information.

pre-trained-and-fine-tuned-models

Economic Value of Fine-Tuning

Fine-tuning holds economic value as it allows businesses to build proprietary custom models, even if the original model was publicly accessible or open source. By leveraging existing foundation models and tailoring them to specific requirements, companies can gain a competitive advantage.

One popular solution to this problem has been fine-tuning, which involves updating the weights or configuration of the base GPT model. But, as OpenAI puts it, the recommended solution for answering technical questions, news, etc., is using “embeddings,” for which they have launched a specific API.

This wayt, fine-tuning would correspond to necessary training to customize more general aspects, such as communication style or vocabulary usage.

At this point, OpenAI recommends using fine-tuning for more general tuning, such as language style and keywords. And use embeddings for company- or industry-specific knowledge. This implies 3 layers of models: pre-trained, fine-tuned, and embeddings.

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Pic: Elastic

Embeddings: A New Layer of Information

For those operating at the application layer, incorporating fine-tuned custom models from the model layer can be a differentiating factor. This can be achieved easily by utilizing managed language model providers that simplify the fine-tuning process. Such providers enable convenient experimentation with numerous custom models, paving the way for innovation and accelerated progress.

With the Embeddings API, we provide a separate document containing relevant information, then find sections in that document similar to the user’s query and create a new prompt with this information to answer the question.

There are multiple use cases for embeddings, including customer service AI Assistants providing answers to frequently asked questions, programming bots that answer developer questions, legal question-answering bots providing accurate responses to legal queries, medical question-answering bots addressing patient inquiries, addressing questions about key points discussed in a meeting, and more.

Embeddings serve as an additional layer of information that we add to the model to handle any specific type of query.

 


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