AI Tool Calling: Enable AI models to Access External Systems

AI tool calling, a function call, is very important for running AI applications. Its primary purpose is to enable the AI model to access information beyond its built-in limits. For example, if you ask an AI model to give the current date and time, it generally cannot predict real-time data due to its limited training data. In such instances, through the AI tool’s calling methodology, these models can interact with external tools, such as online weather reports, and answer the query appropriately.

In a nutshell, AI tool calling is the process of allowing an AI model to connect to or interact with external systems’ APIs to retrieve real-time data.

AI Tool Calling

Importantly, there are two functions for tool calling in AI applications.

While there are different kinds of tool calling, this particular type helps fetch information from external sources, such as a web search engine, a web service, or a database. The goal of this tool call is to make the AI model ready to answer questions that would otherwise be impossible to answer. Some use cases include retrieving real-time weather updates for a specific location or accessing the latest news.

Another use of tool-calling methodology is to enable AI models to automate tasks. It otherwise requires human involvement to complete a software task. For instance, using an external tool, an AI model can reserve a flight seat for a customer who is chatting with a chatbot. Likewise, it can help the customer fill out a contact form on a website.

Throughout this process, the AI model will not take responsibility for executing the tools. It just recommends them.

Although the Model Calls the Tool, the software code performs the logic.

For example, if a user asks ChatGPT, an AI model, for the real-time weather conditions in a particular city, it simply calls the tool. Because it cannot fetch the information on its own, in this example, it just decides the timing and type of the tool requirement.

The AI model takes the user requirement and decides to call a function.

In real time, it runs the function and returns the value to the AI model.

Although the AI model exhibits intelligence, the application executes the requests.

The entire process includes certain security measures. For example, the model cannot access any API tools.

The Application Performs the Tool Call

The application or backend program takes input from the AI model and executes the request. For example, it identifies the tool name as “getweather” and accordingly calls the specific API. Finally, it fetches the request’s result.

Lastly, the Application Returns the Result to the AI Model

After the request is executed, the application must provide the AI model with the final result. Thus, the AI model generates a response to the user.

Discover more from BerylSoft

Subscribe now to keep reading and get access to the full archive.

Continue reading