ChatGPT and other generative AI tools offer the promise to transform the modern economy. In fact, this technical innovation might someday rival smartphones and the Cloud as similar gamechangers from the world of IT. Among many other intriguing use-cases, it provides the ability to optimize the process of customer service, securities trading, and more. Instead of ChatGPT replacing software developers, it serves as an assistant to current employees, helping them become more productive.
An AI-focused research lab, OpenAI, developed ChatGPT and its underlying machine learning model. Microsoft, one of their biggest investors, hit the news recently by integrating ChatGPT into their Bing search engine. The biggest change to this competitor to Google search involves the AI-powered natural language processing of search queries, allowing Bing to answer complex questions with an array of weblinks.
This move from Microsoft raises an important question of how to integrate ChatGPT or a similar generative AI application into your company’s own application family. Let’s take a look at a few use-cases where this integration makes practical sense. At a high level, we also cover what a typical integration project entails. Someday very soon, expect generative AI to become a critical part of your organization’s business operations.
Remember, the business world currently leverages AI in a variety of ways. Interesting use-cases include machine learning models trained to fight cybercrime by detecting suspicious activity, automated securities trading, and even soon to be obsolete customer service chatbots. While these examples rely on detailed machine learning models, ChatGPT also focuses on natural language processing. This allows it to work in a more conversational method, so expect it to quickly supersede the current chatbots used in customer service.
As such, integrating ChatGPT or similar natural language processing functionality makes perfect sense as part of a business’s customer relationship management (CRM) application suite. The conversational aspects of the tool provide suitable communication with current clients and potential customers. As highlighted above, the enhanced natural language processing and generation features far exceed the current AI-based chatbots in various industries.
Other interesting application concepts include virtual assistants, language translation, and text-to-speech generators. Still, similar functionality provides the opportunity to enhance current applications, like the CRM suites mentioned above or even ERP software. Remember, this is only one piece of an overall AI strategy that helps companies save money by optimizing their operations.
In short, ChatGPT and other natural language processing tools allow companies to optimize their customer-facing business functions. These include customer service tools, applicant tracking software in staffing, and supply chain management platforms. Intriguing opportunities abound for organizations with the skills and experience to integrate this form of AI into their existing applications. So let’s provide that high-level overview of a ChatGPT integration project.
Obviously, scoping a project to integrate ChatGPT into an existing business system ultimately depends on the complexity of the application and how you plan on using natural language processing. Still, the actual implementation of ChatGPT remains relatively straightforward if your development team has experience in integrating APIs. Notably, most modern software engineers regularly leverage APIs for a variety of purposes.
OpenAI provides an API for companies to use for the integration of their generative AI tool into existing applications or websites. This availability is already driving an emerging cottage industry of apps leveraging ChatGPT. Thankfully, OpenAI makes starting a ChatGPT integration project easy; just sign up to receive an API key and follow their documentation.
Depending on the requirements of your project, expect to train the GPT-3 machine learning model with text-based data relevant to your business and specific application. For example, if adding an advanced CSR or help chatbot to your app, use textual information related to the app’s functionality. Remember, experience with machine learning model training facilitates this effort.
Be sure to preprocess the data before training the model. This effort cleans up any errors, misspellings, and duplicate content, in addition to formatting the data to be consumed by the model.
At this point, train the model using this data, using a relevant deep learning framework. Expect this to be an iterative process requiring the tweaking of various hyperparameters to tune the model’s performance. Again, this task becomes easier if you boast experience developing and training machine learning models. In short, it’s not a plug-and-play effort.
Once the model is trained, it also needs to be encapsulated in an API wrapper and either deployed on your website or embedded within a desktop or mobile app. You also need to build the user interface and code to calls to the API in the app using the new API. Obviously, engage in a robust QA process to ensure everything works as expected, including retraining the model if necessary.
If your company lacks the resources for machine learning development, integrating ChatGPT becomes more difficult. So partner with NineTwoThree, as we boast extensive experience in machine learning software development. Connect with us to discuss the potential benefits of a partnership.
