As the world continues to embrace artificial intelligence (AI), interactive systems like OpenAI’s ChatGPT have become a popular application of AI and that includes the enterprise sector. These technologies are known commonly as “Generative AI” technologies.

Generative AI has come a long way from their early iterations, and today they are capable of carrying out complex conversations with users, in fluid natural language, and simplify the process of exposing key information as needed. The main reason for this evolution is the advent of Large Language Models (LLMs) that take instructions as input and generate natural language text in response to those instructions. For example, an instruction can be “write letter to my mom wishing her happy birthday” and the response will be the generated letter.

Two Generative AI names that have captured the imagination of the enterprise include OpenAI’s ChatGPT and the GPT API. In this blog post, I will explore the differences between these two and the gaps that exist for deploying them for enterprise use.

ChatGPT is a chat application built upon GPT, the large language model (LLM) trained by OpenAI, that is further trained using a technique called reinforcement learning with human feedback. This further training is necessary as it is meant to be used by the general public so it must choose more ethical and socially acceptable answers. ChatGPT is capable of understanding human language and generating human-like, or natural language responses. ChatGPT improves its responses over time based on curated feedback it receives from users. This technology is a breakthrough for IT systems and their potential to self-improve with normal interactions with humans.  The older ChatGPT is powered by GPT 3.5 whereas the more recent ChatGPT Plus is powered by GPT 4. For this discussion, we will use the term ChatGPT to refer to both the old and the new versions.

On the other hand, GPT API is a cloud-based API that provides access to the same GPT 3.5 and GPT 4 language models that underly ChatGPT and it can be used to generate natural language responses. Enterprises can use the GPT API as an additional model in their suite of deep learning models that constitute their natural language processing pipeline. The GPT 4 model served by the GPT API has already been fine-tuned using reinforcement learning with human feedback. So, enterprises can, in theory, use the GPT 4 model via GPT API to get performance similar to ChatGPT Plus on their proprietary data. For this reason, many recent literature tend to use the term ChatGPT interchangeably with GPT 4. In fact, when Microsoft Azure’s articles and press releases talk about ChatGPT being available via Azure Open AI services, they are referring to GPT 4 being available via Azure Open AI services.

One of the main differences between ChatGPT and GPT API is their deployment options. ChatGPT is meant for public consumption and is hosted by OpenAI. The usage data may be used by OpenAI to improve the models further – this is a big red flag for enterprises and many companies have directed their employees to not use ChatGPT for any company work. On the other hand, there is a version of GPT API hosted by Microsoft Azure with enterprise grade data privacy and security. Enterprises can create isolated instances for GPT API and connect to it from their private cloud which makes it more suitable for enterprises that must adhere to their corporate privacy and data regulations.

Another difference between the two models is their customization capabilities. The GPT API can be fine-tuned to a specific domain or industry using domain and application specific prompts which makes it more suitable for enterprises that require chatbots tailored to their specific needs. The creation of this enterprise and domain specific training data can be an expensive process.

There are gaps that exist for deploying LLMs such as GPT 4 for enterprise use. One of the main gaps is access to corporate IP, product information, support information, or technology documentation which could provide information that would be used to inform the AI system to be able to support company processes. Also, LLMs requires significant computing resources, which can be expensive for enterprises. In addition, enterprises may need to hire specialized talent to deploy and maintain LLMs, which can also be a cost. Having said that, the cost challenge is no different than the characteristics of any other IT system deployment.

Another gap is the lack of data privacy regulations governing the use of LLMs applications like ChatGPT. As AI models become more advanced, there is a growing concern about data privacy and security. This is especially true for enterprises that handle sensitive customer data.  This can be mitigated through sensitive controls on the use of GPT API by an enterprise.

In conclusion, enterprises can benefit from the power of Large Language Models that power ChatGPT and still comply with their data security and privacy requirements --through proper use of the GPT API from Microsoft Azure Open AI services. Thus, if you want to use Generative AI in your enterprise without any security concerns, use GPT from Microsoft Azure Open AI.

For more information on how Quark.AI can complete a Generative AI implementation for your company’s support processes, please visit us at www.quark.ai

Author:

Prosenjit Sen

Founder & CEO

Quark.ai

Email: prosenjitsen10@quark.ai

Linkedin: https://www.linkedin.com/in/prsen/

Website: www.quark.ai

Author’s Bio:

Prosenjit Sen is a technologist and serial entrepreneur. He is currently the CEO of Quark.ai, an Autonomous Support platform that uses Generative AI technology (LLM, NLP, and Computer Vision) to bring automation to Customer Support. He was previously employee # 5 and part of the founding team of Informatica, the worldwide leaders in data integration technology. He is also a Mentor for the Alchemist Accelerator, and the Bay Area IIT Startups Accelerator.

The co-author f "RFID for Energy & Utility Industries”, a bestselling book published by PennWell Publishers, Mr. Sen holds a BS in Electronics Engineering from the Indian Institute of Technology, Kharagpur. A jazz, cooking, and hiking enthusiast, he lives in Silicon Valley with his wife and two daughters.