Exploring the benefits and limitations of generative AI for businesses.
Generative AI is a type of artificial intelligence (AI) system that is designed to generate or create new content, such as text, images, music, or other types of data, based on the patterns it has learned from existing data.
Large language models (LLMs) and generative AI have been making headlines in recent months for their impressive capabilities. However, the use of this technology involves both opportunities and risks that need to be considered. In this article, we will take a closer look at the potential benefits and drawbacks of generative AI from the perspective of enterprise organizations.
Firstly, let's address the risks. Large language models are trained on vast amounts of information, but this information is general and not tailored to specific organizational needs. This means that they may not be able to answer questions with specialized knowledge. Moreover, LLMs can generate false or made-up answers, leading to inaccurate responses.
Additionally, the training of LLMs takes a long time, sometimes months or even years, and they are not connected to other systems. This can result in outdated answers or an inability to perform certain tasks, such as blocking a credit card or booking an appointment.
However, despite these risks, there are still significant opportunities to be gained from the use of generative AI. Large language models have an incredible ability to translate text, improve language, and provide alternative phrasings or tone of voice. They can also generate answers and summarize text, making time-consuming tasks like writing content and inputting data much faster.
By integrating these capabilities into conversational AI platforms, businesses can automate tasks and create value for both themselves and their customers. However, it is important to ensure that the answers provided by the virtual agent are accurate, consistent, and up-to-date. This can be achieved by connecting the model to a source of company data, such as a website, and having a "human in the loop" to verify the information provided by the chatbot, reducing the risks associated with the use of LLMs.
While the potential benefits of this technology are significant, businesses must also be aware of the various drawbacks and take steps to mitigate them. By integrating the versatility of language models with conversational AI platforms and having a human in the loop, businesses can unlock the full potential of generative AI while also ensuring that their chatbots are precise, consistent, and current.
At boost.ai, we've created proprietary algorithms capable of precisely scanning websites and other data sources. These algorithms can also work with LLMs to make them more manageable for enterprise use cases.
Interested in discovering more about how boost.ai is using large language models to enhance customer experiences? Take a look at our latest webinar showcasing a range of LLM-enriched features that will soon be available on our virtual agents. [WATCH HERE]