Generative AI models, especially large language models (LLMs) like ChatGPT, have created a lot of excitement in recent months for their ability to generate human-like language, produce creative writing, write software code, and even perform tasks like translation and summarization. These models can be used for a wide range of applications, from chatbots and virtual assistants to content creation and customer service. However, the potential of these models goes far beyond these initial use cases.
We are just at the beginning of the boost in productivity that these models can bring. LLMs have the potential to revolutionize the way we work and interact with technology. And we are discovering new ways to make them better and use them to solve complex problems.
How can businesses improve their operational efficiency with generative AI? How can the users of products and services leverage generative AI to expedite business outcomes? Along the sames lines what can employees of the business accomplish? Here’s what you need to know about personalized generative AI and how you can link their responses and with relevant actions to realize economic gains.
Why use your own data?
LLMs are incredibly powerful and versatile thanks to their ability to learn from vast amounts of data. However, the data they are trained on is general in nature, covering a wide range of topics and domains. While this allows LLMs to generate high-quality text that is generally accurate and coherent, it also means that they may not perform well in specialized domains that were not included in their training data.
When pushed into your enterprise, LLMs may generate text that is factually inaccurate or even nonsensical. This is because they are trained to generate plausible text based on patterns in the data they have seen, rather than on deep knowledge of the underlying concepts. This phenomenon is called “hallucination,” and it can be a major problem when using LLMs in sensitive fields where accuracy is crucial.
By customizing LLMs with your own data, you can make sure that they become more reliable in the domain of your application and are less likely to generate inaccurate or nonsensical text. Many business require 100% reliable and accurate responses!
Customization can make it possible to use LLMs in sensitive fields where accuracy is very important, such as in healthcare, education, government, and legal. As you improve the quality and accuracy of your model’s output, you can generate actionable responses that users can trust and use to take relevant actions. As the accuracy of the model continues to increase, it goes from knowledge efficiency to operational efficiency, enabling users to streamline or automate actions that previously required intense manual work. This directly translates into time saving, better productivity, and a higher return on investment.
How to personalize LLMs with your own data
There are generally two approaches to customizing LLMs: fine-tuning and retrieval augmentation. Each approach has its own benefits and tradeoffs.
Fine-tuning involves training the LLM with your own data. This means taking a foundation model and training it on a specific set of proprietary data, such as health records, educational material, network logs, or government documents. The benefit of fine-tuning is that the model incorporates your data into its knowledge and can use it in all kinds of prompts. The tradeoff is that fine-tuning can be expensive and technically tricky, as it requires a large amount of high-quality data and significant computing resources.
Retrieval augmentation uses your documents to provide context to the LLM. In this process, every time the user writes a prompt, you retrieve a document that contains relevant information and pass it on to the model along with the user prompt. The model then uses this document as context to draw knowledge and generate more accurate responses. The benefit of retrieval augmentation is that it is easy to set up and doesn’t require retraining the model.
It is also suitable when you’re faced with applications where the context is dynamic and the AI model must tailor its responses to each user based on their data. For example, ahealthcare assistant must personalize its responses based on each user’s health record.
The tradeoff of retrieval augmentation is that it makes prompts longer and increases the costs of inference.
There is also a hybrid approach, where you fine-tune your model with new knowledge every once in a while and use retrieval augmentation to provide it up-to-the-minute context to the model. This approach combines the benefits of both fine-tuning and retrieval augmentation and allows you to keep your model up-to-date with the latest knowledge while also adjusting it to each user’s context.
When choosing an approach, it’s important to consider the specific use case and available resources. Fine-tuning is suitable when you have a large amount of high-quality data and the computing resources to train the model. Retrieval augmentation is suitable when you need dynamic context. The hybrid approach is suitable when you have a specialized knowledge base that is very different from the training dataset of the foundation model and you also have dynamic contexts.
The future of personalized generative AI and generative AI models
The potential of personalized generative AI models is vast and exciting. We’re only at the beginning of the revolution that generative AI will usher in.
We are currently seeing the power of LLMs in providing access to knowledge. By leveraging your own data and tailoring these models to your specific domain, you can improve the accuracy and reliability of their output.
The next step is improving the efficiency of operations. With personalized generative AI, users will be able to tie the output of LLMs to relevant actions that can improve business outcomes. This opens up new possibilities for using LLMs in totally new applications.
Alan’s Actionable AI platform has been built from the ground up to leverage the full potential of personalized generative AI. From providing fine-tuning and augmented retrieval to adding personalized context, Alan AI enables companies to not only customize LLMs to each application and user, but to also link it to specific actions within their software ecosystem. This will be the main driver of improved operational efficiency in times to come.
As Alan AI Platform continues to advance, the possibilities for personalized generative AI models will only continue to expand to deliver the operations efficiency gains for your business.
