Connecting Large Language Models to APIs is a transformative approach to overcome the inherent limitations of these models. By default, LLMs are excellent at processing language but lack the ability to access real-time data or perform actions such as booking flights or sending messages. This limitation can be addressed by enabling LLMs to interact with the outside world, thereby making them more useful.
The video discusses a three-step process to achieve this. The first step involves designing a prompt for the LLM. This prompt guides the LLM to generate a response that includes a command for an external tool. The second step involves writing application code to execute API calls based on the LLM’s command. The final step is feeding the API output back into the LLM. This allows the LLM to generate a user-friendly response based on the data received from the API.
The video also provides an example of building a chatbot with a weather API. It outlines the process of architecting an app with an LLM and a weather API, writing prompts and application code, and feeding the API output back to the LLM. This process enables the chatbot to provide real-time weather updates to users.
Furthermore, the video discusses how to detect and execute tool commands. It explains how to identify when an LLM outputs a tool command, how to execute the command, handle API responses, and return conversational responses to users. This process enhances the LLM’s ability to interact with multiple tools and handle complex queries.